Search results for: deep soil mixing column

Authors: M. O. Bayram, H. B. Gencdal, N. O. Fercan, B. Basbug

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Liquid limit, which is used as a measure of soil strength, can be detected by Casagrande and fall-cone testing methods. The two methods majorly diverge from each other in terms of operator dependency. The Casagrande method that is applied according to ASTM D4318-17 standards may give misleading results, especially if the calibration process is not performed well. To reveal the effect of calibration for drop height and amount of soil paste placement in the Casagrande cup, a series of tests were carried out by multipoint method as it is specified in the ASTM standards. The tests include the combination of 6 mm, 8 mm, 10 mm, and 12 mm drop heights and under-filled, half-filled, and full-filled Casagrande cups by kaolinite samples. It was observed that during successive tests, the drop height of the cup deteriorated; hence the device was recalibrated before and after each test to provide the accuracy of the results. Besides, the tests by under-filled and full-filled samples for higher drop heights revealed lower liquid limit values than the lower drop heights revealed. For the half-filled samples, it was clearly seen that the liquid limit values didn’t change at all as the drop height increased, and this explains the function of standard specifications.

Keywords: calibration, casagrande cup method, drop height, kaolinite, liquid limit, placing form

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Authors: Hai-Hong Gu, Yan-Jun Ai, Zheng Zhou

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Vanadium and titanium are rare metals with superior properties and are important resources in aerospace, aviation, and military. The vanadium/titanium magnetite are mostly ultra-lean ores, and a large number of tailings has been produced in the exploitation process. The tailings are characterized by loose structure, poor nutrient, complex composition and high trace metal contents. Returning green manure has been shown to not only increase plant biomass and soil nutrients but also change the bioavailability of trace metals and the microbial community structure. Fungi play an important role in decomposing organic matter and increasing soil fertility, and the application of organic matter also affects the community structure of fungi. The effects of green manure plants, alfalfa (Medicago sativa L.), returned to the tailings in situ on community structure of fungi, nutrients and bioavailability of trace metals in vanadium/titanium magnetite tailings were investigated in a pot experiment. The results showed that the fungal community diversity and richness were increase after alfalfa green manure returned in situ. The dominant phyla of the fungal community were Ascomycota, Basidiomycota and Ciliophora, especially, the phyla Ciliophora was rare in ordinary soil, but had been found to be the dominant phyla in tailings. Meanwhile, the nutrient properties and various trace metals may shape the microbial communities by affecting the abundance of fungi. It was found that the plant growth was stimulated and the available N and organic C were significantly improved in the vanadium/titanium magnetite tailing with the long-term returning of alfalfa green manure. Moreover, the DTPA-TEA extractable Cd and Zn concentrations in the vanadium/titanium magnetite tailing were reduced by 7.72%~23.8% and 8.02%~24.4%, respectively, compared with those in the non-returning treatment. The above results suggest that the returning of alfalfa green manure could be a potential approach to improve fungal community structure and restore mine tailing ecosystem.

Keywords: fungal community, green manure returning, vanadium/titanium magnet tailings, trace metals

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Authors: D. Kekelidze, G. Tsotadze, G. Maisuradze, L. Akhalbedashvili, M. Chkhaidze

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Statement of Problem: The problem of solid wastes -dangerous sources of environmental pollution,is the urgent issue for Georgia as there are no waste-treatment and waste- incineration plants. Urban peripheral and rural areas, frequently along small rivers, are occupied by landfills without any permission. The study of the pollution of some environmental objects in the adjacent territories of solid waste landfill in Tbilisi carried out in 2020-2021, within the framework of project: “Ecological monitoring of the landfills surrounding areas and population health risk assessment”. Research objects: This research had goal to assess the ecological state of environmental objects (soil cover and surface water) in the territories, adjacent of solid waste landfill, on the base of changes heavy metals' (HM) concentration with distance from landfill. An open sanitary landfill for solid domestic waste in Tbilisi locates at suburb Lilo surrounded with densely populated villages. Content of following HM was determined in soil and river water samples: Pb, Cd, Cu, Zn, Ni, Co, Mn. Methodology: The HM content in samples was measured, using flame atomic absorption spectrophotometry (spectrophotometer of firm Perkin-Elmer AAnalyst 200) in accordance with ISO 11466 and GOST Р 53218-2008. Results and discussion: Data obtained confirmed migration of HM mainly in terms of the distance from the polygon that can be explained by their areal emissions and storage in open state, they could also get into the soil cover under the influence of wind and precipitation. Concentration of Pb, Cd, Cu, Zn always increases with approaching to landfill. High concentrations of Pb, Cd are characteristic of the soil covers of the adjacent territories around the landfill at a distance of 250, 500 meters.They create a dangerous zone, since they can later migrate into plants, enter in rivers and lakes. The higher concentrations, compared to the maximum permissible concentrations (MPC) for surface waters of Georgia, are observed for Pb, Cd. One of the reasons for the low concentration of HM in river water may be high turbidity – as is known, suspended particles are good natural sorbents that causes low concentration of dissolved forms. Concentration of Cu, Ni, Mn increases in winter, since in this season the rivers are switched to groundwater feeding. Conclusion: Soil covers of the areas adjacent to the landfill in Lilo are contaminated with HM. High concentrations in soils are characteristic of lead and cadmium. Elevated concentrations in comparison with the MPC for surface waters adopted in Georgia are also observed for Pb, Cd at checkpoints along and below (1000 m) of the landfill downstream. Data obtained confirm migration of HM to the adjacent territories of the landfill and to the Lochini River. Since the migration and toxicity of metals depends also on the presence of their mobile forms in water bodies, samples of bottom sediments should be taken too. Bottom sediments reflect a long-term picture of pollution, they accumulate HM and represent a constant source of secondary pollution of water bodies. The study of the physicochemical forms of metals is one of the priority areas for further research.

Keywords: landfill, pollution, heavy metals, migration

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Authors: Mohamed Khiatine, Amal Medjnoun, Ramdane Bahar

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The North Algeria is a highly seismic zone, as evidenced by the historical seismicity. During the past two decades, it has experienced several moderate to strong earthquakes. Therefore, the geotechnical engineering problems that involve dynamic loading of soils and soil-structure interaction system requires, in the presence of saturated loose sand formations, liquefaction studies. Bejaia city, located in North-East of Algiers, Algeria, is a part of the alluvial plain which covers an area of approximately 750 hectares. According to the Algerian seismic code, it is classified as moderate seismicity zone. This area had not experienced in the past urban development because of the different hazards identified by hydraulic and geotechnical studies conducted in the region. The low bearing capacity of the soil, its high compressibility and the risk of liquefaction and flooding are among these risks and are a constraint on urbanization. In this area, several cases of structures founded on shallow foundations have suffered damages. Hence, the soils need treatment to reduce the risk. Many field and laboratory investigations, core drilling, pressuremeter test, standard penetration test (SPT), cone penetrometer test (CPT) and geophysical down hole test, were performed in different locations of the area. The major part of the area consists of silty fine sand , sometimes heterogeneous, has not yet reached a sufficient degree of consolidation. The ground water depth changes between 1.5 and 4 m. These investigations show that the liquefaction phenomenon is one of the critical problems for geotechnical engineers and one of the obstacles found in design phase of projects. This paper presents an analysis to evaluate the liquefaction potential, using the empirical methods based on Standard Penetration Test (SPT), Cone Penetration Test (CPT) and shear wave velocity and numerical analysis. These liquefaction assessment procedures indicate that liquefaction can occur to considerable depths in silty sand of harbor zone of Bejaia.

Keywords: earthquake, modeling, liquefaction potential, laboratory investigations

Procedia PDF Downloads 356

Authors: G. Lancellotta, G. Di Salvo, A. Rigazio, A. Davout, V. Pastore, G. Tonoli, A. Martin, P. Jullien, R. Jagow-Klaff, R. Wernecke

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Artificial ground freezing (AGF) technique is a well-proven soil improvement approach used worldwide to construct shafts, tunnels and many other civil structures in difficult subsoil or ambient conditions. As part of the extension of Line 14 of the Paris subway, a passenger interchange tunnel between the new station at Porte de CI ichy and the new Tribunal the Grand Instance has been successfully constructed using this technique. The paper presents the successful application of AGF by Liquid Nitrogen and Brine implemented to provide structural stability and groundwater cut-off around the passenger interchange tunnel. The working conditions were considered to be rather challenging, due to the proximity of a hundred-year-old existing service tunnel of the Line 13, and subsoil conditions on site. Laboratory tests were carried out to determine the relevant soil parameters for hydro-thermal-mechanical aspects and to implement numerical analyses. Monitoring data were used in order to check and control the development and the efficiency of the freezing process as well as to back analyze the parameters assumed for the design, both during the freezing and thawing phases.

Keywords: artificial ground freezing, brine method, case history, liquid nitrogen

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Authors: Punwath Prum

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Superfund site is recognized publicly to be a severe environmental problem to surrounding communities and biodiversity due to its hazardous chemical waste from industrial activities. It contaminates the soil and water but also is a leading potential point-source pollution affecting ecosystem in watershed areas from chemical substances. The risks of Superfund site on watershed can be effectively measured by utilizing publicly available data and geospatial analysis by free and open source application. This study analyzed the vegetation change within high risked contaminated watersheds in Wisconsin. The high risk watersheds were measured by which watershed contained high number Superfund sites. The study identified two potential risk watersheds in Lafayette and analyzed the temporal changes of vegetation within the areas based on Normalized difference vegetation index (NDVI) analysis. The raster statistic was used to compare the change of NDVI value over the period. The analysis results showed that the NDVI value within the Superfund sites’ boundary has a significant lower value than nearby surrounding and provides an analogy for environmental hazard affect by the chemical contamination in Superfund site.

Keywords: soil contamination, spatial analysis, watershed

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: Chao He, Shunhua Zhou, Peijun Guo

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The vast majority of existing underground railway lines consist of twin tunnels. In this paper, the dynamic interaction between two neighboring tunnels in a layered half-space is investigated by an analytical model. The two tunnels are modelled as cylindrical thin shells, while the soil in the form of a layered half-space with two cylindrical cavities is simulated by the elastic continuum theory. The transfer matrix method is first used to derive the relationship between the plane wave vectors in arbitrary layers and the source layer. Thereafter, the wave translation and transformation are introduced to determine the plane and cylindrical wave vectors in the source layer. The solution for the dynamic interaction between twin tunnels in a layered half-space is obtained by means of the compatibility of displacements and equilibrium of stresses on the two tunnel–soil interfaces. By coupling the proposed model with a fully track model, the train-induced vibrations from twin tunnels in a multi-layered half-space are investigated. The numerical results demonstrate that the existence of a neighboring tunnel has a significant effect on ground vibrations.

Keywords: underground railway, twin tunnels, wave translation and transformation, transfer matrix method

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Authors: Colin S. Chen, Chien-Jung Tien, Hsin-Jan Huang

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For weathering studies, the change of chemical constituents by biodegradation effect in diesel-contaminated soils are important factors to be considered, especially when there is a prolonged period of weathering processes. The objective was to evaluate biodegradation effects onto hydrocarbon fingerprinting and distribution patterns of diesel fuels, fuel source screening and differentiation, source-specific marker compounds, and diagnostic ratios of diesel fuel constituents by laboratory and field studies. Biodegradation processes of diesel contaminated soils were evaluated by experiments lasting for 15 and 12 months, respectively. The degradation of diesel fuel in top soils was affected by organic carbon content and biomass of microorganisms in soils. Higher depletion of total petroleum hydrocarbon (TPH), n-alkanes, and polynuclear aromatic hydrocarbons (PAHs) and their alkyl homologues was observed in soils containing higher organic carbon content and biomass. Decreased ratio of selected isoprenoids (i.e., pristane (Pr) and phytane (Ph)) including n-C17/pristane and n-C18/phytane was observed. The ratio of pristane/phytane was remained consistent for a longer period of time. At the end of the experimental period, a decrease of pristane/phytane was observed. Biomarker compounds of bicyclic sesquiterpanes (BS) were less susceptible to the effects of biodegradation. The ratios of characteristic factors such as C15 sesquiterpane/ 8β(H)-drimane (BS3/BS5), C15 sesquiterpane/ 8β(H)-drimane (BS4/BS5), 8β(H)-drimane/8β(H)-homodrimane (BS5/BS10), and C15 sesquiterpane/8β(H)-homodrimane (BS3/BS10) could be adopted for source identification of diesel fuels in top soil. However, for biodegradation processes lasted for six months but shorter than nine months, only BS3/BS5 and BS3/BS10 could be distinguished in two diesel fuels. In subsoil experiments (contaminated soil located 50 cm below), the ratios of characteristic factors including BS3/BS5, BS4/BS5, and BS5/BS10 were valid for source identification of two diesel fuels for nine month biodegradation. At the early stage of contamination, biomass of soil decreased significantly. However, 6 and 7 dominant species were found in soils in top soil experiments, respectively. With less oxygen and nutrients in subsoil, less biomass of microorganisms was observed in subsoils. Only 2 and 4 diesel-degrading species of microorganisms were identified in two soils, respectively. Parameters of double ratio such as fluorene/C1-fluorene: C2-phenanthrene/C3-phenanthrene (C0F/C1F:C2P/C3P) in both top and subsoil, C2-naphthalene/C2-phenanthrene: C1-phenanthrene/C3-phenanthrene (C2N/C2P:C1P/C3P), and C1-phenanthrene/C1-fluorene: C3-naphthalene/C3-phenanthrene (C1P/C1F:C3N/C3P) in subsoil could serve as forensic indicators in diesel contaminated sites. BS3/BS10:BS4/BS5 could be used in 6 to 9 months of biodegradation processes. Results of principal component analysis (PCA) indicated that source identification of diesel fuels in top soil could only be perofrmed for weathering process less than 6 months. For subsoil, identification can be conducted for weathering process less than 9 months. Ratio of isoprenoids (pristane and phytane) and PAHs might be affected by biodegradation in spilled sites. The ratios of bicyclic sesquiterpanes could serve as forensic indicators in diesel-contaminated soils. Finally, source identification was attemped for samples collected from different fuel contaminated sites by using the unique pattern of sesquiterpanes. It was anticipated that the information generated from this study would be adopted by decision makers to evaluate the liability of cleanup in diesel contaminated sites.

Keywords: biodegradation, diagnostic ratio, diesel fuel, environmental forensics

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Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

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Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

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Authors: Recep Gundogan, Turgay Dindaroglu, Hikmet Gunal, Mustafa Ulukavak, Ron Bingner

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A majority of total soil losses in agricultural areas could be attributed to ephemeral gullies caused by heavy rains in conventionally tilled fields; however, ephemeral gully erosion is often ignored in conventional soil erosion assessments. Ephemeral gullies are often easily filled from normal soil tillage operations, which makes capturing the existing ephemeral gullies in croplands difficult. This study was carried out to determine topographic features, including slope and aspect composite topographic index (CTI) and initiation points of gully channels, using images obtained from unmanned aerial vehicle (UAV) images. The study area was located in Topcu stream watershed in the eastern Mediterranean Region, where intense rainfall events occur over very short time periods. The slope varied between 0.7 and 99.5%, and the average slope was 24.7%. The UAV (multi-propeller hexacopter) was used as the carrier platform, and images were obtained with the RGB camera mounted on the UAV. The digital terrain models (DTM) of Topçu stream micro catchment produced using UAV images and manual field Global Positioning System (GPS) measurements were compared to assess the accuracy of UAV based measurements. Eighty-one gully channels were detected in the study area. The mean slope and CTI values in the micro-catchment obtained from DTMs generated using UAV images were 19.2% and 3.64, respectively, and both slope and CTI values were lower than those obtained using GPS measurements. The total length and volume of the gully channels were 868.2 m and 5.52 m³, respectively. Topographic characteristics and information on ephemeral gully channels (location of initial point, volume, and length) were estimated with high accuracy using the UAV images. The results reveal that UAV-based measuring techniques can be used in lieu of existing GPS and total station techniques by using images obtained with high-resolution UAVs.

Keywords: aspect, compound topographic index, digital terrain model, initial gully point, slope, unmanned aerial vehicle

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: T. A. Adesiyan, J. A. Adekoya A. Akinlua, N. Torto

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One hundred and eighty six in situ soil samples of the B–horizon were collected around Gbongan–Odeyinka-Orileowu area, southwestern Nigeria, delineated by longitude 4°15l and 4°30l and latitude 7°14l and 7°31 for a reconnaissance geochemical soil survey. The objective was to determine the distribution pattern of some trace metals in the area with a view to discovering any indication of metallic mineralization. The samples were air–dried and sieved to obtain the minus 230 µ fractions which were used for pH determinations and subjected to hot aqua regia acid digestion. The solutions obtained were analyzed for Ag, As, Au, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sn, and Zn using atomic absorption spectrometric methods. The resulting data were subjected to simple statistical treatment and used in preparing distribution maps of the elements. With these, the spatial distributions of the elements in the area were discussed. The pH of the soils range from 4.70 to 7.59 and this reflects the geochemical distribution patterns of trace metals in the area. The spatial distribution maps of the elements showed similarity in the distributions of Co, Cr, Fe, Ni, Mn and Pb. This suggests close associations between these elements none of which showed any significant anomaly in the study. The associations might be due to the scavenging actions of Fe–Mn oxides on the elements. Only Ag, Au and Sn on one hand and Zn on the other hand showed significant anomalies, which are thought to be due to mineralization and anthropogenic activities respectively.

Keywords: distribution, metals, Gbongan, Nigeria, mineralization anthropogenic

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Authors: Pouya Taraghi, Yong Li, Nader Yoosef-Ghodsi, Muntaseer Kainat, Samer Adeeb

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Buried pipelines play a crucial role in the transportation of energy products such as oil, gas, and various chemical fluids, ensuring their efficient and safe distribution. However, these pipelines are often susceptible to ground movements caused by geohazards like landslides, fault movements, lateral spreading, and more. Such ground movements can lead to strain-induced failures in pipes, resulting in leaks or explosions, leading to fires, financial losses, environmental contamination, and even loss of human life. Therefore, it is essential to study how buried pipelines respond when traversing geohazard-prone areas to assess the potential impact of ground movement on pipeline design. As such, this study introduces an approach called the Physics-Informed Neural Network (PINN) to predict the strain demand in inelastic pipes subjected to permanent ground displacement (PGD). This method uses a deep learning framework that does not require training data and makes it feasible to consider more realistic assumptions regarding existing nonlinearities. It leverages the underlying physics described by differential equations to approximate the solution. The study analyzes various scenarios involving different geohazard types, PGD values, and crossing angles, comparing the predictions with results obtained from finite element methods. The findings demonstrate a good agreement between the results of the proposed method and the finite element method, highlighting its potential as a simulation-free, data-free, and meshless alternative. This study paves the way for further advancements, such as the simulation-free reliability assessment of pipes subjected to PGD, as part of ongoing research that leverages the proposed method.

Keywords: strain demand, inelastic pipe, permanent ground displacement, machine learning, physics-informed neural network

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Authors: V. Ivanov, J. Chu, V. Stabnikov

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Different biotechnological methods for the production of construction materials and for the performance of construction processes in situ are developing within a new scientific discipline of Construction Biotechnology. The aim of this research was to develop and test new biotechnologies and biotechnological grouts for the minimization of the hydraulic conductivity of the fractured rocks and porous soil. This problem is essential to minimize flow rate of groundwater into the construction sites, the tunneling space before and after excavation, inside levies, as well as to stop water seepage from the aquaculture ponds, agricultural channels, radioactive waste or toxic chemicals storage sites, from the landfills or from the soil-polluted sites. The conventional fine or ultrafine cement grouts or chemical grouts have such restrictions as high cost, viscosity, sometime toxicity but the biogrouts, which are based on microbial or enzymatic activities and some not expensive inorganic reagents, could be more suitable in many cases because of lower cost and low or zero toxicity. Due to these advantages, development of biotechnologies for biogrouting is going exponentially. However, most popular at present biogrout, which is based on activity of urease- producing bacteria initiating crystallization of calcium carbonate from calcium salt has such disadvantages as production of toxic ammonium/ammonia and development of high pH. Therefore, the aim of our studies was development and testing of new biogrouts that are environmentally friendly and have low cost suitable for large scale geotechnical, construction, and environmental applications. New microbial biotechnologies have been studied and tested in the sand columns, fissured rock samples, in 1 m3 tank with sand, and in the pack of stone sheets that were the models of the porous soil and fractured rocks. Several biotechnological methods showed positive results: 1) biogrouting using sequential desaturation of sand by injection of denitrifying bacteria and medium following with biocementation using urease-producing bacteria, urea and calcium salt decreased hydraulic conductivity of sand to 2×10-7 ms-1 after 17 days of treatment and consumed almost three times less reagents than conventional calcium-and urea-based biogrouting; 2) biogrouting using slime-producing bacteria decreased hydraulic conductivity of sand to 1x10-6 ms-1 after 15 days of treatment; 3) biogrouting of the rocks with the width of the fissures 65×10-6 m using calcium bicarbonate solution, that was produced from CaCO3 and CO2 under 30 bars pressure, decreased hydraulic conductivity of the fissured rocks to 2×10-7 ms-1 after 5 days of treatment. These bioclogging technologies could have a lot of advantages over conventional construction materials and processes and can be used in geotechnical engineering, agriculture and aquaculture, and for the environmental protection.

Keywords: biocementation, bioclogging, biogrouting, fractured rocks, porous soil, tunneling space

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Authors: Tasleem Zafar, Jiwan Sidhu

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Introduction: Chickpea flour is known to offer many benefits to diabetic persons, especially in maintaining their blood sugar levels in the acceptable range. Under this project we have studied the chemical composition and antioxidant capacity of white flour (WF), whole wheat flour (WWF) and chickpea flour (BF), in addition to the effect of replacement of WF and WWF with BF on the rheological characteristics of these flour blends, with the ultimate objective of producing acceptable quality flat as well as pan-bread for the diabetic consumers. Methods: WF and WWF were replaced with BF ranging from 0 to 40%, to investigate its effect on the rheological properties and functionality of blended flour dough using farinograph, viscoamylograph, mixograph and falling number apparatus as per the AACC standard methods. Texture Profile Analysis (TPA) was carried on the WF, WWF, and their blends with BF using Stable Micro System Texture Analyzer. Effect of certain additives, such as freeze-dried amla fruit powder (Phyllanthus emblica L.), guar gum, and xanthan gum on the dough rheological properties were also studied. Results: Freeze-dried amla fruit powder was found to be very rich in ascorbic acid and other phenolics having higher antioxidant activity. A decreased farinograph water absorption, increased dough development time, higher mixing tolerance index (i.e., weakening of dough), decreased resistance to extension, lower ratio numbers were obtained when the replacement with BF was increased from 0 to 40%. The BF gave lower peak viscosity, lower paste breakdown, and lower setback values when compared with WF. The falling number values were significantly lower in WWF (meaning higher α-amylase activity) than both the WF and BF. Texture Profile Analysis (TPA) carried on the WF, WWF, and their blends with BF showed significant variations in hardness and compressibility values, dough becoming less hard and less compressible when the replacement of WF and WWF with BF was increased from 0 to 40%. Conclusions: To overcome the deleterious effects of adding BF to WF and WWF on the rheological properties will be an interesting challenge when good quality pan bread and Arabic flatbread have to be commercially produced in a bakery. Use of freeze-dried amla fruit powder, guar gum, and xanthan gum did show some promise to improve the mixing characteristics of WF, WWF, and their blends with BF, and these additives are expected to be useful in producing an acceptable quality flat as well as pan-bread on a commercial scale.

Keywords: wheat flour, chickpea flour, amla fruit, rheology

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Authors: Bruno Cossu, Elio Carlo

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The invention (patent pending) relates to the field of devices aimed to harness wave energy (WEC) especially for artificial upwelling, forced downwelling, production of compressed air. In its basic form, the pump consists of a hydro-pneumatic machine, driven by wave energy, characterised by the fact that it has no moving mechanical parts, and is made up of only two structural components: an hollow body, which is open at the bottom to the sea and partially immersed in sea water, and a tube, both joined together to form a single body. The shape of the hollow body is like a mushroom whose cap and stem are hollow; the stem is open at both ends and the lower part of its surface is crossed by holes; the tube is external and coaxial to the stem and is joined to it so as to form a single body. This shape of the hollow body and the type of connection to the tube allows the pump to operate simultaneously as an air compressor (OWC) on the cap side, and as an airlift on the stem side. The pump can be implemented in four versions, each of which provides different variants and methods of implementation: 1) firstly, for the artificial upwelling of cold, deep ocean water; 2) secondly, for the lifting and transfer of these waters to the place of use (above all, fish farming plants), even if kilometres away; 3) thirdly, for the forced downwelling of surface sea water; 4) fourthly, for the forced downwelling of surface water, its oxygenation, and the simultaneous production of compressed air. The transfer of the deep water or the downwelling of the raised surface water (as for pump versions indicated in points 2 and 3 above), is obtained by making the water raised by the airlift flow into the upper inlet of another pipe, internal or adjoined to the airlift; the downwelling of raised surface water, oxygenation, and the simultaneous production of compressed air (as for the pump version indicated in point 4), is obtained by installing a venturi tube on the upper end of the pipe, whose restricted section is connected to the external atmosphere, so that it also operates like a hydraulic air compressor (trompe). Furthermore, by combining one or more pumps for the upwelling of cold, deep water, with one or more pumps for the downwelling of the warm surface water, the system can be used in an Ocean Thermal Energy Conversion plant to supply the cold and the warm water required for the operation of the same, thus allowing to use, without increased costs, in addition to the mechanical energy of the waves, for the purposes indicated in points 1 to 4, the thermal one of the marine water treated in the process.

Keywords: air lifted upwelling, fish farming plant, hydraulic air compressor, wave energy converter

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Authors: H. Gonzalez-Rivera, J. L. Palmeros-Torres

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This paper describes the application of traditional computer vision techniques as a procedure for automatic measurement of the secondary dendrite arm spacing (SDAS) from microscopic images. The algorithm is capable of finding the lineal or curve-shaped secondary column of the main microstructure, measuring its length size in a micro-meter and counting the number of spaces between dendrites. The automatic characterization was compared with a set of 1728 manually characterized images, leading to an accuracy of −0.27 µm for the length size determination and a precision of ± 2.78 counts for dendrite spacing counting, also reducing the characterization time from 7 hours to 2 minutes.

Keywords: dendrite arm spacing, microstructure inspection, pattern recognition, polynomial regression

Procedia PDF Downloads 52

Authors: Zeina Bourhane, Anders Lanzen, Christine Cagnon, Olfa Ben Said, Cristiana Cravo-Laureau, Robert Duran

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The anthropogenic pressure in coastal areas increases dramatically with the exploitation of environmental resources. Biomonitoring coastal areas are crucial to determine the impact of pollutants on bacterial communities in soils and sediments since they provide important ecosystem services. However, relevant biomonitoring tools allowing fast determination of the ecological status are yet to be defined. Microbial ecology approaches provide useful information for developing such microbial monitoring tools reporting on the effect of environmental stressors. Chemical and microbial molecular approaches were combined in order to determine microbial bioindicators for assessing the ecological status of soil and river ecosystems around the Ichkeul Lake (Tunisia), an area highly impacted by human activities. Samples were collected along soil/river/lake continuums in three stations around the Ichkeul Lake influenced by different human activities at two seasons (summer and winter). Contaminant pressure indexes (PI), including PAHs (Polycyclic aromatic hydrocarbons), alkanes, and OCPs (Organochlorine pesticides) contents, showed significant differences in the contamination level between the stations with seasonal variation. Bacterial communities were characterized by 16S ribosomal RNAs (rRNA) gene metabarcoding. Although microgAMBI indexes, determined from the sequencing data, were in accordance with contaminant contents, they were not sufficient to fully explain the PI. Therefore, further microbial indicators are still to be defined. The comparison of bacterial communities revealed the specific microbial assemblage for soil, river, and lake sediments, which were significantly correlated with contaminant contents and PI. Such observation offers the possibility to define a relevant set of bioindicators for reporting the effects of human activities on the microbial community structure. Such bioindicators might constitute useful monitoring tools for the management of microbial communities in coastal areas.

Keywords: bacterial communities, biomonitoring, contamination, human impacts, microbial bioindicators

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Authors: A. M. Tahsini

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Computational study of two dimensional supersonic reacting hydrogen-air flows is performed to investigate the nitrogen effects on ignition delay time for premixed and diffusion flames. Chemical reaction is treated using detail kinetics and the advection upstream splitting method is used to calculate the numerical inviscid fluxes. The results show that only in the stoichiometric condition for both premixed and diffusion flames, there is monotone dependency of the ignition delay time to the nitrogen addition. In other situations, the optimal condition from ignition viewpoint should be found using numerical investigations.

Keywords: diffusion flame, ignition delay time, mixing layer, numerical simulation, premixed flame, supersonic flow

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Authors: Carol N. Flores-Fernández, Guadalupe Espilco, Cynthia Esquerre, Amparo I. Zavaleta

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Saline environments represent a valuable source of enzymes with novel properties and particular features for application in food, pharmaceutical and chemical industry. This study focuses on the isolation and screening of hydrolase-producing bacteria from Chilca salterns and the evaluation of their biotechnological potential. Soil samples were collected from Chilca salterns in Peru. For the isolation, medium containing 0.2 % of yeast extract, 5 % of NaCl and 10 % of the soil sample was used. After 72 h of incubation at 37 °C, serial dilutions were made up to 10−12 dilutions, spread on agar plates with 0.5 % of yeast extract and 5 % of NaCl, and incubated at 37 °C for 48 h. Screening of hydrolase-producing bacteria was carried out for cellulases, amylases, lipases, DNase, and proteases on specific media. Moreover, protease-producing bacteria were tested using protein extracted from the following legumes as substrate: Glycine max, Lupinus mutabilis, Pisum sativum, Erythrina edulis, Cicer arietinum, Phaseolus vulgaris and Vicia faba. A total of 16 strains were isolated from soil samples. On the screening media; 75, 44, 81 and 50 % were cellulase, amylase, DNase and protease producers, respectively. Also, 19 % of the isolates produced all the hydrolytic enzymes above mentioned. Lipase producers were not found. The 37 % and 12 % of the strains grew at 20 % and 30 % of salt concentration, respectively. In addition, 75 % of the strains grew at pH range between 5 and 10. From the total of protease-producing bacteria, 100 % hydrolyzed Glycine max, Lupinus mutabilis, and Pisum sativum protein, while 87 % hydrolyzed Erythrina edulis and Cicer arietinum protein. Finally, 75 % and 50 % of the strains hydrolyzed Phaseolus vulgaris and Vicia faba protein, respectively. Hydrolase-producing bacteria isolated from Chilca salterns in Peru grew at high salt concentrations and wide range of pH. In addition, protease-producing bacteria hydrolyzed protein from different sources such as leguminous. These enzymes have great biotechnological potential and could be used for different industrial processes and applications.

Keywords: bacteria, extracellular, hydrolases, Peru, salterns

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Authors: T. Hamaishi, Y. Morinaga, H. Morita

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Introduction: Rice consumption in Japan has decreased, and Japanese government has recommended use of rice flour in order to expand the consumption of rice. There are two major protein components present in flour, called gliadin and glutenin. Gluten forms when water is added to flour and is mixed. As mixing continues, glutenin interacts with gliadin to form viscoelastic matrix of gluten. Rice flour bread does not expand as much as wheat flour bread. Because rice flour is not included gluten, it cannot construct gluten network in the dough. In recent years, some food additives have been used for dough-improving agent in bread making, especially surfactants has effect in order to improve dough extensibility. Therefore, we focused to fatty acid salt which is one of anionic surfactants. Fatty acid salt is a salt consist of fatty acid and alkali, it is main components of soap. According to JECFA(FAO/WHO Joint Expert Committee on Food Additives), salts of Myristic(C14), Palmitic(C16) and Stearic(C18) could be used as food additive. They have been evaluated ADI was not specified. In this study, we investigated to improving bread-making properties of rice flour dough adding fatty acid salt. Materials and methods: The sample of fatty acid salt is myristic (C14) dissolved in KOH solution to a concentration of 350 mM and pH 10.5. Rice dough was consisted of 100 g of flour using rice flour and wheat gluten, 5 g of sugar, 1.7 g of salt, 1.7g of dry yeast, 80 mL of water and fatty acid salt. Mixing was performed for 500 times by using hand. The concentration of C14K in the dough was 10 % relative to flour weight. Amount of gluten in the dough was 20 %, 30 % relative to flour weight. Dough expansion ability test was performed to measure physical property of bread dough according to the methods of Baker’s Yeast by Japan Yeast Industry Association. In this test, 150 g of dough was filled from bottom of the cylinder and fermented at 30 °C，85 % humidity for 120 min on an incubator. The height of the expansion in the dough was measured and determined its expansion ability. Results and Conclusion: Expansion ability of rice dough with gluten content of 20 %, 30% showed 316 mL, 341 mL for 120 min. When C14K adding to the rice dough, dough expansion abilities were 314 mL, 368 mL for 120 min, there was no significant difference. Conventionally it has been known that the rice flour dough contain gluten of 20 %. The considerable improvement of dough expansion ability was achieved when added C14K to wheat flour. The experimental result shows that c14k adding to the rice dough with gluten content more than 20 % was not improving bread-making properties. In conclusion, rice bread made with gluten content more than 20 % without C14K has been suggested to contribute to the formation of the sufficient gluten network.

Keywords: expansion ability, fatty acid salt, gluten, rice flour dough

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Authors: Samuel Emeka Anarah, Kingsley Nnaemeka Ogbu, Obasi Arinze

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Quantifying the hydrological response due to changes in climate change is imperative for proper management of water resources within a watershed. The impact of climate change on the hydrology of the Upper Ebony River (UER) watershed, South East Nigeria, was studied using the Soil and Water Assessment Tool (SWAT) hydrological model. A climatological time series analysis from 1985 - 2014 using non-parametric test showed significant negative trends in precipitation and relative humidity trend while minimum and maximum temperature, solar radiation and wind speed showed significant positive trends. Future hypothetical land-use change scenarios (Scenarios 1, 2, 3 and 4) representing urbanization and conversion of forest to agricultural land were combined with future downscaled climate model (CSIRO-Mk3-6-0) and simulated in SWAT model. Relative to the Baseline scenario (2005 - 2014), the results showed a decrease in streamflow by 10.29%, 26.20%, 11.80% and 26.72% for Scenarios 1, 2, 3, and 4 respectively. Model results suggest development of adaptation strategies to cope with the predicted hydrological conditions under future climate change in the watershed.

Keywords: climate change, hydrology, runoff, SWAT model

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Authors: Shivam Yadav, Neelam Atri

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Cyanobacteria, the photosynthetic prokaryotes are indispensable components of paddy soil contribute substantially to the nitrogen economy however often appended with metal load. They are well known to play crucial roles in maintenance of soil fertility and rice productivity. Nickel is one such metal that plays a vital role in the cellular physiology, however at higher concentrations it exerts adverse effects. Arsenic is another toxic metalloid that negatively affects the cyanobacterial proliferation. However species-specific comparative responses under As and Ni is largely unknown. The present study focuses on the comparative effects of nickel (Ni2+) and arsenite (As(III)) on two diazotrophic cyanobacterial species (Anabaena doliolum and Anabaena sp. PCC7120) in terms of antioxidative aspects. Oxidative damage measured in terms of lipid peroxidation and peroxide content was significantly higher after As(III) than Ni treatment as compared to control. Similarly, all the studied enzymatic and non-enzymatic parameters of antioxidative defense system except glutathione reductase (GR) showed greater induction against As(III) than Ni. Moreover, integrating comparative analysis of all studied parameters also demonstrated interspecies variation in terms of stress adaptive strategies reflected through higher sensitivity of Anabaena doliolum over Anabaena PCC7120.

Keywords: antioxidative system, arsenic, cyanobacteria, nickel

Procedia PDF Downloads 156

Authors: Nathaporn Areerachakul

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In this study, the adsorption capacity of GAC with metsulfuron methyl was evaluated by using adsorption equilibrium and a fixed bed. Mathematical modelling was also used to simulate the GAC adsorption behavior. Adsorption equilibrium experiment of GAC was conducted using a constant concentration of metsulfuron methyl of 10 mg/L. The purpose of this study was to find the single component equilibrium concentration of herbicide. The adsorption behavior was simulated using the Langmuir, Freundlich, and Sips isotherm. The Sips isotherm fitted the experimental data reasonably well with an error of 6.6 % compared with 15.72 % and 7.07% for the Langmuir isotherm and Freudrich isotherm. Modelling using GAC adsorption theory could not replicate the experimental results in fixed bed column of 10 and 15 cm bed depths after a period more than 10 days of operation. This phenomenon is attributed to the formation of micro-organism (BAC) on the surface of GAC in addition to GAC alone.

Keywords: isotherm, adsorption equilibrium, GAC, metsulfuron methyl

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Authors: Anika Zafiah M. Rus, Nur Munirah Abdullah, M. F. L. Abdullah

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The fabrication and characterization of composite films of graphite- bioepoxy is described. Free-standing thin films of ~0.1 mm thick are prepared using a simple solution mixing with mass proportion of 7/3 (bioepoxy/graphite) and drop casting at room temperature. Fourier transform infra-red spectroscopy (FTIR) and Ultraviolet-visible (UV-vis) spectrophotometer are performed to evaluate the changes in chemical structure and adsorption spectra arising with the increasing of graphite weight loading (wt.%) into the biopolymer matrix. The morphologic study shows a homogeneously dispersed and strong particle bonding between the graphite and the bioepoxy, with conductivity of the film 103 S/m, confirming the efficiency of the processes.

Keywords: absorbance peak, biopolymer, graphite- bioepoxy composites, particle bonding

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Authors: Ime R. Udotong, Ofonime U. M. John, Justina I. R. Udotong

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Ibeno, Nigeria hosts the operational base of Mobil Producing Nigeria Unlimited (MPNU), a subsidiary of ExxonMobil and the current highest oil and condensate producer in Nigeria. Besides MPNU, other multinational oil companies like Shell Petroleum Development Company Ltd, Elf Petroleum Nigeria Ltd and Nigerian Agip Energy, a subsidiary of ENI E&P operate onshore, on the continental shelf and deep offshore of the Atlantic Ocean in Ibeno, Nigeria, respectively. This study was designed to carry out the survey of the oil impacted sites in Ibeno, Nigeria. A combinations of electrical resistivity (ER), ground penetrating radar (GPR) and physico-chemical as well as microbiological characterization of soils and water samples from the area were carried out. Results obtained revealed that there have been hydrocarbon contaminations of this environment by past crude oil spills as observed from significant concentrations of THC, BTEX and heavy metal contents in the environment. Also, high resistivity values and GPR profiles clearly showing the distribution, thickness and lateral extent of hydrocarbon contamination as represented on the radargram reflector tones corroborates previous significant oil input. Contaminations were of varying degrees, ranging from slight to high, indicating levels of substantial attenuation of crude oil contamination over time. Hydrocarbon pollution of the study area was confirmed by the results of soil and water physico-chemical and microbiological analysis. The levels of THC contamination observed in this study are indicative of high levels of crude oil contamination. Moreover, the display of relatively lower resistivities of locations outside the impacted areas compared to resistivity values within the impacted areas, the 3-D Cartesian images of oil contaminant plume depicted by red, light brown and magenta for high, low and very low oil impacted areas, respectively as well as the high counts of hydrocarbonoclastic microorganisms in excess of 1% confirmed significant recent pollution of the study area.

Keywords: oil-polluted sites, physico-chemical analyses, microbiological characterization, geotechnical investigations, total hydrocarbon content

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Authors: Endalkachew Abebe Kebede, Bojin Bojinov, Andon Vasilev Andonov, Orhan Dengiz

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Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing sensors against in-situ field spectral measurement. The current study assessed the potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of the wheat crop on a study farm found in the village of OvchaMogila. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 to April 2022. An Unmanned Aerial Vehicle (UAV) has been used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. The ten most common vegetation indices have been selected and calculated based on the reflectance wavelength range of remote sensing tools used. The soil samples have been collected in eight different locations within the farm plot. The different physicochemical properties of the soil (pH, texture, N, P₂O₅, and K₂O) have been analyzed in the laboratory. The finer resolution images from the UAV and the Leaf Spectrometer have been used to validate the satellite images. The performance of different sensors has been compared based on the measured leaf spectral response and the extracted vegetation indices using the five sampling points. A scatter plot with the coefficient of determination (R2) and Root Mean Square Error (RMSE) and the correlation (r) matrix prepared using the corr and heatmap python libraries have been used for comparing the performance of Sentinel 2 and Landsat 9 VIs compared to the drone and SpectraVue 710s spectrophotometer. The soil analysis revealed the study farm plot is slightly alkaline (8.4 to 8.52). The soil texture of the study farm is dominantly Clay and Clay Loam.The vegetation indices (VIs) increased linearly with the growth of the plant. Both the scatter plot and the correlation matrix showed that Sentinel 2 vegetation indices have a relatively better correlation with the vegetation indices of the Buteo dronecompared to the Landsat 9. The Landsat 9 vegetation indices somewhat align better with the leaf spectrometer. Generally, the Sentinel 2 showed a better performance than the Landsat 9. Further study with enough field spectral sampling and repeated UAV imaging is required to improve the quality of the current study.

Keywords: landsat 9, leaf spectrometer, sentinel 2, UAV

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Authors: Kumar Divyendra

Abstract:

Underwater Sensor Networks (UWSNs) have wide applications in areas such as water quality monitoring, marine wildlife management etc. A typical UWSN system consists of a set of sensors deployed randomly underwater which communicate with each other using acoustic links. RF communication doesn't work underwater, and GPS too isn't available underwater. Additionally Automated Underwater Vehicles (AUVs) are deployed to collect data from some special nodes called Cluster Heads (CHs). These CHs aggregate data from their neighboring nodes and forward them to the AUVs using optical links when an AUV is in range. This helps reduce the number of hops covered by data packets and helps conserve energy. We consider the three-dimensional model of the UWSN. Nodes are initially deployed randomly underwater. They attach themselves to the surface using a rod and can only move upwards or downwards using a pump and bladder mechanism. We use graph theory concepts to maximize the coverage volume while every node maintaining connectivity with at least one surface node. We treat the surface nodes as landmarks and each node finds out its hop distance from every surface node. We treat these hop-distances as coordinates and use them for AUV navigation. An AUV intending to move closer to a node with given coordinates moves hop by hop through nodes that are closest to it in terms of these coordinates. In absence of GPS, multiple different approaches like Inertial Navigation System (INS), Doppler Velocity Log (DVL), computer vision-based navigation, etc., have been proposed. These systems have their own drawbacks. INS accumulates error with time, vision techniques require prior information about the environment. We propose a method that makes use of the earth's magnetic field values for navigation and combines it with other methods that simultaneously increase the coverage volume under the UWSN. The AUVs are fitted with magnetometers that measure the magnetic intensity (I), horizontal inclination (H), and Declination (D). The International Geomagnetic Reference Field (IGRF) is a mathematical model of the earth's magnetic field, which provides the field values for the geographical coordinateson earth. Researchers have developed an inverse deep learning model that takes the magnetic field values and predicts the location coordinates. We make use of this model within our work. We combine this with with the hop-by-hop movement described earlier so that the AUVs move in such a sequence that the deep learning predictor gets trained as quickly and precisely as possible We run simulations in MATLAB to prove the effectiveness of our model with respect to other methods described in the literature.

Keywords: clustering, deep learning, network backbone, parallel computing

Procedia PDF Downloads 100

Authors: John Davidson, Matteo Castelletti, Ismael Torres, Victor Terente, Jamie Irvine, Sylvie Raymackers

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The current industry approach to pile driving predictions consists of developing a model of the hammer-pile-soil system which simulates the relationship between soil resistance to driving (SRD) and blow counts (or pile penetration per blow). The SRD methods traditionally used are broadly based on static pile capacity calculations. The SRD is used in combination with the one-dimensional wave equation model to indicate the anticipated blowcounts with depth for specific hammer energy settings. This approach has predominantly been calibrated on relatively long slender piles used in the oil and gas industry but is now being extended to allow calculations to be undertaken for relatively short rigid large diameter monopile foundations. This paper evaluates the accuracy of current industry practice when applied to a site where large diameter monopiles were installed in predominantly stiff fissured clay. Actual geotechnical and pile installation data, including pile driving records and signal matching analysis (based upon pile driving monitoring techniques), were used for the assessment on the case study site.

Keywords: driven piles, fissured clay, London clay, monopiles, offshore foundations

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