Search results for: fire prediction model

Authors: Carolina Morales, Ricardo Castro-Huerta, Enrique A. Mundaca

Abstract:

The edaphic fauna is the main factor involved in the transformation of nutrients and soil decomposition processes. Edaphic organisms are highly sensitive to soil disturbances, which normally causes changes in the composition and abundance of such organisms. Fire is known to be a disturbing factor since it affects the physical, chemical and biological properties of the soil and the whole ecosystem. During the summer (December-March) of 2017, Chile suffered the major fire events recorded in its modern history, which affected a vast area and a number of ecosystem types. The objective of this study was first to use remote sensing satellite images and GIS (Geographic Information Systems) to assess and identify levels of fire severity in disturbed areas and to compare the responses of the soil mesofauna diversity among such areas. We identified four areas (treatments) with an ascending level of severity, namely: mild, medium, high severity, and free of fire. A non-affected patch of forest was established as a control. Three samples from each treatment were collected in the form of a soil cube (10x10x10 cm). Edaphic mesofauna was obtained from each sample through the Berlese-Tullgren funnel method. Collected specimens were quantified and identified, using the RTU (Recognisable Taxonomic Unit) criterion. Diversity was analysed using inferential statistics to compare Simpson and Shannon-Wiener indexes across treatments. As predicted, the unburned forest patch (control) exhibited higher diversity values than the treatments. Significantly higher diversity values were recorded in those treatments subjected to lower fire severity. We conclude that remote sensing zoning is an adequate tool to identify different levels of fire severity and that an edaphic mesofauna is a group of organisms that qualify as good bioindicators for monitoring soil recovery after fire events.

Keywords: bioindicator, Chile, fire severity level, soil

Procedia PDF Downloads 149

Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

Abstract:

Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

Procedia PDF Downloads 83

Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim, Rashidah Ghazali, Noramli Abdul Razak

Abstract:

Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of prediction models has facilitated the estimation process in recent years. In this study, 110 crude palm oil (CPO) samples were used to build a free fatty acid (FFA) prediction model. 60% of the collected data were used for training purposes and the remaining 40% used for testing. The visible peaks on the NIR spectrum were at 1725 nm and 1760 nm, indicating the existence of the first overtone of C-H bands. Principal component regression (PCR) was applied to the data in order to build this mathematical prediction model. The optimal number of principal components was 10. The results showed R2=0.7147 for the training set and R2=0.6404 for the testing set.

Keywords: palm oil, fatty acid, NIRS, regression

Procedia PDF Downloads 494

Authors: Osung Im, Neha Yadav, Eui Hoon Lee, Joong Hoon Kim

Abstract:

Artificial Neural Network (ANN) approach is commonly used in lots of fields for forecasting. In water resources engineering, forecast of water level or inflow of reservoir is useful for various kind of purposes. Due to advantages of ANN, many papers were written for inflow prediction in river networks, but in this study, ANN is used in urban sewer networks. The growth of severe rain storm in Korea has increased flood damage severely, and the precipitation distribution is getting more erratic. Therefore, effective pump operation in pump station is an essential task for the reduction in urban area. If real time inflow of pump station reservoir can be predicted, it is possible to operate pump effectively for reducing the flood damage. This study used ANN model for pump station reservoir inflow prediction using upstream sewer depth data. For this study, rainfall events, sewer depth, and inflow into Banpo pump station reservoir between years of 2013-2014 were considered. Feed – Forward Back Propagation (FFBF), Cascade – Forward Back Propagation (CFBP), Elman Back Propagation (EBP) and Nonlinear Autoregressive Exogenous (NARX) were used as ANN model for prediction. A comparison of results with ANN model suggests that ANN is a powerful tool for inflow prediction using the sewer depth data.

Keywords: artificial neural network, forecasting, reservoir inflow, sewer depth

Procedia PDF Downloads 300

Authors: Fikret Yalcinkaya, Hamza Unsal

Abstract:

To understand how neurons work, it is required to combine experimental studies on neural science with numerical simulations of neuron models in a computer environment. In this regard, the simplicity and applicability of spiking neuron modeling functions have been of great interest in computational neuron science and numerical neuroscience in recent years. Spiking neuron models can be classified by exhibiting various neuronal behaviors, such as spiking and bursting. These classifications are important for researchers working on theoretical neuroscience. In this paper, three different spiking neuron models; Izhikevich, Adaptive Exponential Integrate Fire (AEIF) and Hindmarsh Rose (HR), which are based on first order differential equations, are discussed and compared. First, the physical meanings, derivatives, and differential equations of each model are provided and simulated in the Matlab environment. Then, by selecting appropriate parameters, the models were visually examined in the Matlab environment and it was aimed to demonstrate which model can simulate well-known biological neuron behaviours such as Tonic Spiking, Tonic Bursting, Mixed Mode Firing, Spike Frequency Adaptation, Resonator and Integrator. As a result, the Izhikevich model has been shown to perform Regular Spiking, Continuous Explosion, Intrinsically Bursting, Thalmo Cortical, Low-Threshold Spiking and Resonator. The Adaptive Exponential Integrate Fire model has been able to produce firing patterns such as Regular Ignition, Adaptive Ignition, Initially Explosive Ignition, Regular Explosive Ignition, Delayed Ignition, Delayed Regular Explosive Ignition, Temporary Ignition and Irregular Ignition. The Hindmarsh Rose model showed three different dynamic neuron behaviours; Spike, Burst and Chaotic. From these results, the Izhikevich cell model may be preferred due to its ability to reflect the true behavior of the nerve cell, the ability to produce different types of spikes, and the suitability for use in larger scale brain models. The most important reason for choosing the Adaptive Exponential Integrate Fire model is that it can create rich ignition patterns with fewer parameters. The chaotic behaviours of the Hindmarsh Rose neuron model, like some chaotic systems, is thought to be used in many scientific and engineering applications such as physics, secure communication and signal processing.

Keywords: Izhikevich, adaptive exponential integrate fire, Hindmarsh Rose, biological neuron behaviours, spiking neuron models

Procedia PDF Downloads 166

Authors: Sagir M. Yusuf, Chris Baber

Abstract:

In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

Procedia PDF Downloads 107

Authors: Matt Stallwood, Michael Spearpoint

Abstract:

In 2020, the UK government announced that sales of new petrol and diesel cars would end in 2030, and battery-powered cars made up 1 in 8 new cars sold in 2021 – more than the total from the previous five years. The guidance across the UK for the fire safety design of covered car parks is changing in response to the projected rapid growth in electric vehicle (EV) use. This paper discusses the current knowledge on the fire safety concerns posed by EVs, in particular those powered by lithium-ion batteries, when considering the likelihood of vehicle ignition, fire severity and spread of fire to other vehicles. The paper builds on previous work that has investigated the frequency of fires starting in cars powered by internal combustion engines (ICE), the hazard posed by such fires in covered car parks and the potential for neighboring vehicles to become involved in an incident. Historical data has been used to determine the ignition frequency of ICE car fires, whereas such data is scarce when it comes to EV fires. Should a fire occur, then the fire development has conventionally been assessed to match a ‘medium’ growth rate and to have a 95th percentile peak heat release of 9 MW. The paper examines recent literature in which researchers have measured the burning characteristics of EVs to assess whether these values need to be changed. These findings are used to assess the risk posed by EVs when compared to ICE vehicles. The paper examines what new design guidance is being issued by various organizations across the UK, such as fire and rescue services, insurers, local government bodies and regulators and discusses the impact these are having on the arrangement of parking bays, particularly in residential and mixed-use buildings. For example, the paper illustrates how updated guidance published by the Fire Protection Association (FPA) on the installation of sprinkler systems has increased the hazard classification of parking buildings that can have a considerable impact on the feasibility of a building to meet all its design intents when specifying water supply tanks. Another guidance on the provision of smoke ventilation systems and structural fire resistance is also presented. The paper points to where further research is needed on the fire safety risks posed by EVs in covered car parks. This will ensure that any guidance is commensurate with the need to provide an adequate level of life and property safety in the built environment.

Keywords: covered car parks, electric vehicles, fire safety, risk

Procedia PDF Downloads 65

Authors: Domingos Viegas, Miguel Almeida, Carmen Rocha, Ilda Novo, Yolanda Luna

Abstract:

Forest fires along the more than 1200km of the Spanish-Portuguese border are more and more frequent, currently achieving around 2000 fire events per year. Some of these events develop to large international wildfire requiring concerted operations based on shared information between the two countries. The fire event of Valencia de Alcantara (2003) causing several fatalities and more than 13000ha burnt, is a reference example of these international events. Currently, Portugal and Spain have a specific cross-border cooperation protocol on wildfires response for a strip of about 30km (15 km for each side). It is recognized by public authorities the successfulness of this collaboration however it is also assumed that this cooperation should include more functionalities such as the development of a common risk information system for transboundary fire events. Since Portuguese and Spanish authorities use different approaches to determine the fire risk indexes inputs and different methodologies to assess the fire risk, sometimes the conjoint firefighting operations are jeopardized since the information is not harmonized and the understanding of the situation by the civil protection agents from both countries is not unique. Thus, a methodology aiming the harmonization of the fire risk calculation and perception by Portuguese and Spanish Civil protection authorities is hereby presented. The final results are presented as well. The fire risk index used in this work is the Canadian Fire Weather Index (FWI), which is based on meteorological data. The FWI is limited on its application as it does not take into account other important factors with great effect on the fire appearance and development. The combination of these factors is very complex since, besides the meteorology, it addresses several parameters of different topics, namely: sociology, topography, vegetation and soil cover. Therefore, the meaning of FWI values is different from region to region, according the specific characteristics of each region. In this work, a methodology for FWI calibration based on the number of fire occurrences and on the burnt area in the transboundary regions of Portugal and Spain, in order to assess the fire risk based on calibrated FWI values, is proposed. As previously mentioned, the cooperative firefighting operations require a common perception of the information shared. Therefore, a common classification of the fire risk for the fire events occurred in the transboundary strip is proposed with the objective of harmonizing this type of information. This work is integrated in the ECHO project SpitFire - Spanish-Portuguese Meteorological Information System for Transboundary Operations in Forest Fires, which aims the development of a web platform for the sharing of information and supporting decision tools to be used in international fire events involving Portugal and Spain.

Keywords: data harmonization, FWI, international collaboration, transboundary wildfires

Procedia PDF Downloads 238

Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

Abstract:

Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma

Procedia PDF Downloads 139

Authors: Ki-seok Kwon, Dong-woo Ryu, Heung-Youl Kim

Abstract:

Although high-strength concrete has many advantages over generic concrete at normal temperatures (around 20℃), it undergoes spalling at high temperatures, which constitutes its structurally fatal drawback. In this study, fire resistance tests were conducted for 3 hours in accordance with ASTM E119 on bearing wall specimens which were 3,000mm x 3,000mm x 300mm in dimensions to investigate the influence the type of admixtures would exert on the fire resistance performance of high-strength concrete. Portland cement, blast furnace slag, fly ash and silica fume were used as admixtures, among which 2 or 3 components were combined to make 7 types of mixtures. In 56MPa specimens, the severity of spalling was in order of SF5 > F25 > S65SF5 > S50. Specimen S50 where an admixture consisting of 2 components was added did not undergo spalling. In 70MPa specimens, the severity of spalling was in order of SF5 > F25SF5 > S45SF5 and the result was similar to that observed in 56MPa specimens. Acknowledgements— This study was conducted by the support of the project, “Development of performance-based fire safety design of the building and improvement of fire safety” (18AUDP-B100356-04) which is under the management of Korea Agency for Infrastructure Technology Advancement as part of the urban architecture research project for the Ministry of Land, Infrastructure and Transport, for which we extend our deep thanks.

Keywords: high strength concrete, mineral admixture, fire resistance, social disaster

Procedia PDF Downloads 132

Authors: Dionisios Panagiotaras, Ioannis P. Kokkoris, Dionysios Koulougliotis, Dimitra Lekka, Alexandra Skalioti

Abstract:

In the summer of 2021, Greece was affected by devastating forest fires in various regions of the country, resulting in human losses, destruction or degradation of the natural environment, infrastructure, livestock and cultivations. The present study concerns a pilot assessment of natural vegetation regeneration in the second, in terms of area, fire-affected region for 2021, at Ancient Olympia area, located in West Peloponnisos (Ilia Prefecture), Greece. A standardised field sampling protocol for assessing natural regeneration was implemented at selected sites where the forest fire had occurred previously (in 2007), and the vegetation (Pinus halepensis forest) had regenerated naturally. The results of the study indicate the loss of the established natural regeneration of Pinus halepensis forest, as well as of the tree-layer in total. Post-fire succession species are recorded to the shrub and the herb layer, with a varying cover. Present findings correspond to the results of field work and analysis one year after the fire, which will form the basis for further research and conclusions on taking action for restoration schemes in areas that have been affected by fire more than once within a 20-year period.

Keywords: forest, pinus halepensis, ancient olympia, post fire vegetation

Procedia PDF Downloads 74

Authors: Elmo Thiao Lins Cöuras Ford, Valentina Alessandra Carvalho do Vale, R. M. Nascimento, J. U. L. Mendes

Abstract:

Now the great majority of the applications of thermal isolation in the strip of drops and averages temperatures (up to 200ºC), it is made being used from aggressive materials to the nature such an as: glass wool, rock wool, polystyrene, EPS among others. Such materials, in spite of the effectiveness in the retention of the flow of heat, possess considerable cost and when discarded they are long years to be to decompose. In that context, trying to adapt the world politics the about of the preservation of the environment, a study began with intention of developing a material composite, with properties of thermal, originating from insulating industrial residues. In this research, the behavior of the composite was analyzed, as submitted the fire. For this, the reaction rehearsals were accomplished to the fire for the composites 2:1; 1:1; 1:2 and for the latex, based in the "con" experiment in agreement with the norm ASTM–E 1334-90. As consequence, in function of the answers of the system, was possible to observe to the acting of each mixture proportion.

Keywords: composite, Latex, reacion to the fire, thermal isolation

Procedia PDF Downloads 424

Authors: Megan Alkazoff, Charles Ruffner

Abstract:

Tallgrass prairies and sand savannas, once covering northern to central Illinois, are ecosystems in need of restoration and conservation in the Midwestern United States. The Nature Conservancy manages five sites containing fragments of remaining tallgrass prairies and sand savannas within the Kankakee Sands using techniques such as prescribed burning and invasive species removal. The objective of this study was to conduct a ten-year resampling of transects established on these five sites during previous studies to assess whether the management tools applied there are helping maintain the tallgrass prairie and sand savannas. During the summer of 2020, permanent transect lines were sampled using a quadrat to determine the % Cover Class of each species rooted in the quadrat. Data gathered was analyzed using linear regression to illustrate the relationship between fire occurrence and species composition on the landscape. The fire frequency had a highly significant effect (P= 0.0025) on the species richness of all sites. The frequency of fire had a non-significant effect (P>0.05) on the Floristic Quality Index, percent C value 4-10, and bare-ground percentage of a site. These results suggest that fire on the landscape, both wild and prescribed, have increased biodiversity on all five sites but has not affected the Floristic Quality Index, percent C value 4-10, and the percentage of bare-ground on the sites.

Keywords: fire, floristic quality assessment, sand savanna, species richness, tallgrass prairie

Procedia PDF Downloads 169

Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

Abstract:

This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

Procedia PDF Downloads 131

Authors: Mingjun Xu, Man Pun Wan

Abstract:

This study proposed an effective smoke control strategy for the large flat space with a low ceiling to achieve the requirement of perpetual tenability. For the large flat space with a low ceiling, the depth of the smoke reservoir is very shallow, and it is difficult to perpetually constrain the smoke within a limited space. A series of numerical tests were conducted to determine the smoke strategy. A zonal design i.e., the fire zone and two adjacent zones was proposed and validated to be effective in controlling smoke. Once a fire happens in a compartment space, the Engineered Smoke Control (ESC) system will be activated in three zones i.e., the fire zone, in which the fire happened, and two adjacent zones. The smoke can be perpetually constrained within the three smoke zones. To further improve the extraction efficiency, sequential activation of the ESC system within the 3 zones turned out to be more efficient than simultaneous activation. Additionally, the proposed zonal and sequential extraction design can reduce the mechanical extraction flow rate by up to 40.7 % as compared to the conventional method, which is much more economical than that of the conventional method.

Keywords: performance-based design, perpetual tenability, smoke control, fire plume

Procedia PDF Downloads 58

Authors: Hao Yan, Xiaobing Zhang

Abstract:

The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

Procedia PDF Downloads 77

Authors: Hyun Ah Yoon, Ji Yeon Kang, Hee Sun Kim, Yeong Soo Shin

Abstract:

Reinforced concrete (RC) shear wall system of residential buildings is popular in South Korea. RC walls are subjected to axial forces in common and the effect of axial forces on the strength loss of the fire damaged walls has not been investigated. This paper aims at investigating temperature distribution on fire damaged concrete walls having different axial loads. In the experiments, a variable of specimens is axial force ratio. RC walls are fabricated with 150mm of wall thicknesses, 750mm of lengths and 1,300mm of heights having concrete strength of 24MPa. After curing, specimens are heated on one surface with ISO-834 standard time-temperature curve for 2 hours and temperature distributions during the test are measured using thermocouples inside the walls. The experimental results show that the temperature of the RC walls exposed to fire increases as axial force ratio increases. To verify the experiments, finite element (FE) models are generated for coupled temperature-structure analyses. The analytical results of thermal behaviors are in good agreement with the experimental results. The predicted displacement of the walls decreases when the axial force increases. 

Keywords: axial force ratio, fire, reinforced concrete wall, residual strength

Procedia PDF Downloads 448

Authors: Henok Hailemariam, Frank Wuttke

Abstract:

Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

Procedia PDF Downloads 346

Authors: Yeo Kyeong Lee, Hae Won Min, Ji Yeon Kang, Hee Sun Kim, Yeong Soo Shin

Abstract:

Fire incidents have been steadily increased over the last year according to national emergency management agency of South Korea. Even though most of the fire incidents with property damage have been occurred in building, rehabilitation has not been properly done with consideration of structure safety. Therefore, this study aims at evaluating rehabilitation effects on fire damaged normal strength concrete beams through experiments and finite element analyses. For the experiments, reinforced concrete beams were fabricated having designed concrete strength of 21 MPa. Two different cover thicknesses were used as 40 mm and 50 mm. After cured, the fabricated beams were heated for 1hour or 2hours according to ISO-834 standard time-temperature curve. Rehabilitation was done by removing the damaged part of cover thickness and filling polymeric mortar into the removed part. Both fire damaged beams and rehabilitated beams were tested with four point loading system to observe structural behaviors and the rehabilitation effect. To verify the experiment, finite element (FE) models for structural analysis were generated using commercial software ABAQUS 6.10-3. For the rehabilitated beam models, integrated temperature-structural analyses were performed in advance to obtain geometries of the fire damaged beams. In addition to the fire damaged beam models, rehabilitated part was added with material properties of polymeric mortar. Three dimensional continuum brick elements were used for both temperature and structural analyses. The same loading and boundary conditions as experiments were implemented to the rehabilitated beam models and non-linear geometrical analyses were performed. Test results showed that maximum loads of the rehabilitated beams were 8~10% higher than those of the non-rehabilitated beams and even 1~6 % higher than those of the non-fire damaged beam. Stiffness of the rehabilitated beams were also larger than that of non-rehabilitated beams but smaller than that of the non-fire damaged beams. In addition, predicted structural behaviors from the analyses also showed good rehabilitation effect and the predicted load-deflection curves were similar to the experimental results. From this study, both experiments and analytical results demonstrated good rehabilitation effect on the fire damaged normal strength concrete beams. For the further, the proposed analytical method can be used to predict structural behaviors of rehabilitated and fire damaged concrete beams accurately without suffering from time and cost consuming experimental process.

Keywords: fire, normal strength concrete, rehabilitation, reinforced concrete beam

Procedia PDF Downloads 500

Authors: Idris Haruna Muhammad, Okorie Austine Uche

Abstract:

This paper presents a study on reliability of shear connector exposed to fire situation in accordance with Eurocode 4. The reliability analysis i reliability analysis is based on First Order Second Moment Integration Technique (FOSMIT) using FORM 5. Performance functions for shear connector are derived for normal and under fire condition and their implied safety levels are evaluated. Four (4) design variables which include ultimate tensile strength, diameter of the stud, temperature and span of the steel beam are treated as random variables with their statistical characteristic adopted from literature. Results show that for normal condition the β – value decrease from 7.95 to 5.43 which show it is conservative in safety level for normal condition. Under fire condition, β – value decrease from 2.88 to – 0.32 with corresponding load ratio of 0.2 to 1.2. It was also shown from sensitivity assessment, that the temperature and span of the beam decrease with increase in their β – values while ultimate tensile strength and diameter of the stud increase with increase in their β – values for a given load ratio of 0.2 to 1.2.

Keywords: Composite steel beam, Fire condition, Shear stud, Sensitivity study

Procedia PDF Downloads 509

Authors: Sofia Ait Bouazza

Abstract:

We consider the problem of finding a minimum identifying code in a graph. This problem was initially introduced in 1998 and has been since fundamentally connected to a wide range of applications (fault diagnosis, location detection …). Suppose we have a building into which we need to place fire alarms. Suppose each alarm is designed so that it can detect any fire that starts either in the room in which it is located or in any room that shares a doorway with the room. We want to detect any fire that may occur or use the alarms which are sounding to not only to not only detect any fire but be able to tell exactly where the fire is located in the building. For reasons of cost, we want to use as few alarms as necessary. The first problem involves finding a minimum domination set of a graph. If the alarms are three state alarms capable of distinguishing between a fire in the same room as the alarm and a fire in an adjacent room, we are trying to find a minimum locating domination set. If the alarms are two state alarms that can only sound if there is a fire somewhere nearby, we are looking for a differentiating domination set of a graph. These three areas are the subject of much active research; we primarily focus on the third problem. An identifying code of a graph G is a dominating set C such that every vertex x of G is distinguished from other vertices by the set of vertices in C that are at distance at most r≥1 from x. When only vertices out of the code are asked to be identified, we get the related concept of a locating dominating set. The problem of finding an identifying code (resp a locating dominating code) of minimum size is a NP-hard problem, even when the input graph belongs to a number of specific graph classes. Therefore, we study this problem in some restricted classes of undirected graphs like split graph, line graph and path in a directed graph. Then we present some results on the identifying code by giving an exact value of upper total locating domination and a total 2-identifying code in directed and undirected graph. Moreover we determine exact values of locating dominating code and edge identifying code of thin headless spider and locating dominating code of complete suns.

Keywords: identiying codes, locating dominating set, split graphs, thin headless spider

Procedia PDF Downloads 465

Authors: Xiangtuo Chen, Paul-Henry Cournéde

Abstract:

Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

Procedia PDF Downloads 221

Authors: M. Arifuzzaman, H. S. Kim

Abstract:

Multiple sets of variables associated with expanded perlite particle consolidation in foam manufacturing were analyzed to develop a model for predicting perlite foam density. The consolidation of perlite particles based on the flotation method and compaction involves numerous variables leading to the final perlite foam density. The variables include binder content, compaction ratio, perlite particle size, various perlite particle densities and porosities, and various volumes of perlite at different stages of process. The developed model was found to be useful not only for prediction of foam density but also for optimization between compaction ratio and binder content to achieve a desired density. Experimental verification was conducted using a range of foam densities (0.15–0.5 g/cm3) produced with a range of compaction ratios (1.5-3.5), a range of sodium silicate contents (0.05–0.35 g/ml) in dilution, a range of expanded perlite particle sizes (1-4 mm), and various perlite densities (such as skeletal, material, bulk, and envelope densities). A close agreement between predictions and experimental results was found.

Keywords: expanded perlite, flotation method, foam density, model, prediction, sodium silicate

Procedia PDF Downloads 396

Authors: Pradeep Singh, Shrish Verma

Abstract:

Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

Procedia PDF Downloads 326

Authors: Hernani Brinati, Mardel de Conti, Moyses Szajnbok, Valentina Domiciano

Abstract:

This study brings a mathematical model and examples for the numerical prediction of submersible maneuvers in the horizontal and in the vertical planes. The geometry of the submarine is here taken as a body of revolution plus a sail, two horizontal and two vertical rudders. The model includes the representation of the hull resistance and of the propeller thrust and torque, what enables to consider the variation of the longitudinal component of the velocity of the ship when maneuvering. The hydrodynamic forces are represented through power series expansions of the acceleration and velocity components. The hydrodynamic derivatives for the body of revolution are mostly estimated based on fundamental principles applicable to the flow around airplane fuselages in the subsonic regime. The hydrodynamic forces for the sail and rudders are estimated based on a finite aspect ratio wing theory. The objective of this study is to build an expedite model for submarine maneuvers prediction, based on fundamental principles, which may be convenient in the early stages of the ship design. This model is tested against available numerical and experimental data.

Keywords: submarine maneuvers, submarine, maneuvering, dynamics

Procedia PDF Downloads 623

Authors: Haozhe Xiang

Abstract:

With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

Procedia PDF Downloads 51

Authors: Mohammed Sulaiman, Hexu Liu, Mohamed Binalhaj, William W. Liou, Osama Abudayyeh

Abstract:

Emerging technologies such as camera-equipped unmanned aerial vehicles (UAVs) are increasingly being applied in building fire rescue to provide real-time visualization and 3D reconstruction of the entire fireground. However, flight planning of camera-equipped UAVs is usually a manual process, which is not sufficient to fulfill the needs of emergency management. This research proposes a Geographic Information System (GIS)-based approach to automatic flight planning of camera-equipped UAVs for building fire emergency response. In this research, Haversine formula and lawn mowing patterns are employed to automate flight planning based on geometrical and spatial information from GIS. The resulting flight mission satisfies the requirements of 3D reconstruction purposes of the fireground, in consideration of flight execution safety and visibility of camera frames. The proposed approach is implemented within a GIS environment through an application programming interface. A case study is used to demonstrate the effectiveness of the proposed approach. The result shows that flight mission can be generated in a timely manner for application to fire emergency response.

Keywords: GIS, camera-equipped UAVs, automatic flight planning, fire emergency response

Procedia PDF Downloads 109

Authors: H. Yilmaz Atay

Abstract:

Huntite and hydromagnesite minerals have been used as additive materials to achieve incombustible material due to their inflammability property. Those fire retardants materials can help to extinguish in the early stages of fire. Thus dispersion of the flame can be prevented even if the fire started. Huntite and hydromagnesite minerals are known to impart fire-proofing of the polymer composites. However, the additives used in the applications led to deterioration in the mechanical properties due to the usage of high amount of the powders in the composites. In this study, by enriching huntite and hydromagnesite, it was aimed to use purer minerals to reinforce the polymer composites. Thus, predictably, using purer mineral will lead to use lower amount of mineral powders. By this manner, the minerals free from impurities by various processes were added to the polymer matrix with different loading level and grades. Different types of samples were manufactured, and subsequently characterized by XRD, SEM-EDS, XRF and flame-retardant tests. Tensile strength and elongation at break values were determined according to loading levels and grades. Besides, a comparison on the properties of the polymer composites produced by using of minerals with and without impurities was performed. As a result of the work, it was concluded that it is required to use beneficiated minerals to provide better fire-proofing behaviors in the polymer composites.

Keywords: flame retardant, huntite and hydromagnesite, mechanical property, polymer composites

Procedia PDF Downloads 228

Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

Abstract:

Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

Procedia PDF Downloads 79

Authors: Cristian Păuna

Abstract:

After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

Procedia PDF Downloads 171