Search results for: Truss structure optimization

Authors: Georges Ghazi, Marc-Henry Devillers, Ruxandra M. Botez

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Aircraft flight trajectory optimization has been identified to be a promising solution for reducing both airline costs and the aviation net carbon footprint. Nowadays, this role has been mainly attributed to the flight management system. This system is an onboard multi-purpose computer responsible for providing the crew members with the optimized flight plan from a destination to the next. To accomplish this function, the flight management system uses a variety of look-up tables to compute the optimal speed and altitude for each flight regime instantly. Because the cruise is the longest segment of a typical flight, the proposed algorithm is focused on minimizing fuel consumption for this flight phase. In this paper, a complete methodology to estimate the aircraft performance and subsequently compute the optimal speed in cruise is presented. Results showed that the obtained performance database was accurate enough to predict the flight costs associated with the cruise phase.

Keywords: Cessna Citation X, cruise speed optimization, flight cost, cost index, and golden section search

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Authors: Assad Rashid, Umair Ahmed, Zafar Baig

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A new trend in construction is widely influenced by the use of Steel-Concrete Composite Columns. The rapid growth in Steel-Concrete Composite construction has widely decreased the conventional R.C.C structures. Steel Concrete composite construction has obtained extensive receiving around the globe. It is considering the fact that R.C.C structures construction is most suitable and economical for low-rise construction, so it is used in farming systems in most of the buildings. However, increased dead load, span restriction, less stiffness and risky formwork make R.C.C construction uneconomical and not suitable when it comes to intermediate to high-rise buildings. A Base + Ground +11 storey commercial building was designed on ETABS 2017 and made a comparison between conventional R.C.C and encased composite column structure. After performing Equivalent Static non-linear analysis, it has been found that construction cost is 13.01% more than R.C.C structure but encased composite column building has 7.7% more floor area. This study will help in understanding the behavior of conventional R.C.C structure and Encased Composite column structure.

Keywords: composite columns structure, equivalent static non-linear analysis, comparison between R.C.C and encased composite column structures, cost-effective structure

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Authors: Zeev Wiesman, Paula Berman, Nitzan Meiri, Charles Linder

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Supramolecular chemistry refers to the domain of chemistry beyond that of molecules and focuses on the chemical systems made up of a discrete number of assembled molecular sub units or components. Biodiesel components self arrangements is closely related/affect their physical properties in combustion systems and emission. Due to technological difficulties, knowledge regarding the molecular packing of FAMEs (biodiesel) in the liquid phase is limited. Spectral tools such as X-ray and NMR are known to provide evidences related to molecular structure organization. Recently, it was reported by our research group that using 1H Time Domain NMR methodology based on relaxation time and self diffusion coefficients, FAMEs clusters with different motilities can be accurately studied in the liquid phase. Head to head dimarization with quasi-smectic clusters organization, based on molecular motion analysis, was clearly demonstrated. These findings about the assembly/packing of the FAME components are directly associated with fluidity/viscosity of the biodiesel. Furthermore, these findings may provide information of micro/nano-particles that are formed in the delivery and injection system of various combustion systems (affected by thermodynamic conditions). Various relevant parameters to combustion such as: distillation/Liquid Gas phase transition, cetane number/ignition delay, shoot, oxidation/NOX emission maybe predicted. These data may open the window for further optimization of FAME/diesel mixture in terms of combustion and emission.

Keywords: supermolecular chemistry, FAMEs, liquid phase, fluidity, LF-NMR

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Authors: Mohamed M. El Gendy, Ibrahim A. El Arabi, Rafeek W. Abdel-Missih, Omar A. Kandil

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Nonlinear analysis is one of the most important design and safety tools in structural engineering. Based on the finite-element method, a geometrical and material nonlinear analysis of large span reinforced concrete arches is carried out considering soil-structure interaction. The concrete section details and reinforcement distribution are taken into account. The behavior of soil is considered via Winkler's and continuum models. A computer program (NARC II) is specially developed in order to follow the structural behavior of large span reinforced concrete arches up to failure. The results obtained by the proposed model are compared with available literature for verification. This work confirmed that the geometrical and material nonlinearities, as well as soil structure interaction, have considerable influence on the structural response of reinforced concrete arches.

Keywords: nonlinear analysis, reinforced concrete arched structure, soil-structure interaction, geotechnical engineering

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Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: Sonveer Singh, Sanjay Agrawal, D. V. Avasthi, Jayant Shekhar

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The aim of this paper is to improve the efficiency of photovoltaic thermal (PVT) system with the help of Genetic Algorithms with multi-objective function. There are some parameters that affect the efficiency of PVT system like depth and length of the channel, velocity of flowing fluid through the channel, thickness of the tedlar and glass, temperature of inlet fluid i.e. all above parameters are considered for optimization. An attempt has been made to the model and optimizes the parameters of glazed hybrid single channel PVT module when two objective functions have been considered separately. The two objective function for optimization of PVT module is overall electrical and thermal efficiency. All equations for PVT module have been derived. Using genetic algorithms (GAs), above two objective functions of the system has been optimized separately and analysis has been carried out for two cases. Two cases are: Case-I; Improvement in electrical and thermal efficiency when overall electrical efficiency is optimized, Case-II; Improvement in electrical and thermal efficiency when overall thermal efficiency is optimized. All the parameters that are used in genetic algorithms are the parameters that could be changed, and the non-changeable parameters, like solar radiation, ambient temperature cannot be used in the algorithm. It has been observed that electrical efficiency (14.08%) and thermal efficiency (19.48%) are obtained when overall thermal efficiency was an objective function for optimization. It is observed that GA is a very efficient technique to estimate the design parameters of hybrid single channel PVT module.

Keywords: genetic algorithm, energy, exergy, PVT module, optimization

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Authors: K. Tahir, C. Belfedal, T. Allaoui, C. Gerard, M. Doumi

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In this paper, we presented an optimized RST controller using Particle Swarm Optimization (PSO) meta-heuristic technique of the active and reactive power regulation of a grid connected wind turbine based on a wound field synchronous generator. This regulation is achieved below the synchronous speed, by means of a maximum power point tracking algorithm. The control of our system is tested under typical wind variations and parameters variation, fault grid condition by simulation. Some results are presented and discussed to prove simplicity and efficiency of the WRSG control for WECS. On the other hand, according to simulation results, variable speed driven WRSG is not significantly impacted in fault conditions.

Keywords: wind energy, particle swarm optimization, wound rotor synchronous generator, power control, RST controller, maximum power point tracking

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Authors: Bastien Batardière, Joon Kwon

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For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

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Authors: Bounar Nedjemeddine

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Materials with the pyrochlore lattice structure have attracted much recent attention due to their wide applications in ceramic thermal barrier coatings, high-permittivity dielectrics, and potential solid electrolytes in solid-oxide fuel cells. The work described in this paper is devoted to the synthesis and characterization of a pyrochlore structure based on lanthanum (La₂O₃) and tin (SnO₂) oxides of general formula La₂Sn₂O₇, substituted by Sr at the site La. Their structures were determined from X-ray powder diffraction using CELFER analysis. All the compositions present the space group Fd-3m. The substitution of La by Sr in the La₂Sn₂O₇ compound causes a variation of the cell parameters. The difference in charge between La³⁺ and Sr²⁺ and the difference in size cause the cell parameters to decrease from a=10.7165 A° to a=10.6848 A° for the substitution rates (x = 0.05, 0.1, 0.15 ...), which leads to a decrease in the volume of the mesh. For a substitution rate x = 0.25, there is an increase in the cell parameters (a=10.7035A°), which can be explained by a competitiveness of the size effect and the presence of a gap in the structure which go in the opposite direction.

Keywords: solid-oxide fuel cells, structure, pyrochlore, X-ray diffraction

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Authors: Yogish Huchaiah, Chandrashekara Krishnappa

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This study considers the optimization and validation of experimental results using Multi-Functional Criteria Technique (MFCT). MFCT is concerned with structuring and solving decision and planning problems involving multiple variables. Production of biodiesel from Composite Oil Methyl Esters (COME) of Jatropha and Pongamia oils, mixed in various proportions and Biodiesel thus obtained from two step transesterification process were tested for various Physico-Chemical properties and it has been ascertained that they were within limits proposed by ASTME. They were blended with Petrodiesel in various proportions. These Methyl Esters were blended with Petrodiesel in various proportions and coded. These blends were used as fuels in a computerized CI DI engine to investigate Performance and Emission characteristics. From the analysis of results, it was found that 180MEM4B20 blend had the maximum Performance and minimum Emissions. To validate the experimental results, MFCT was used. Characteristics such as Fuel Consumption (FC), Brake Power (BP), Brake Specific Fuel Consumption (BSFC), Brake Thermal Efficiency (BTE), Carbon dioxide (CO2), Carbon Monoxide (CO), Hydro Carbon (HC) and Nitrogen oxide (NOx) were considered as dependent variables. It was found from the application of this method that the optimized combination of Injection Pressure (IP), Mix and Blend is 178MEM4.2B24. Overall corresponding variation between optimization and experimental results was found to be 7.45%.

Keywords: COME, IP, MFCT, optimization, PI, PN, PV

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Authors: Abdolsalam Ghaderi

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In this paper, the single-depot capacitated location-routing problem under uncertainty is presented. The problem aims to find the optimal location of a single depot and the routing of vehicles to serve the customers when the parameters may change under different circumstances. This problem has many applications, especially in the area of supply chain management and distribution systems. To get closer to real-world situations, travel time of vehicles, the fixed cost of vehicles usage and customers’ demand are considered as a source of uncertainty. A combined approach including robust optimization and stochastic programming was presented to deal with the uncertainty in the problem at hand. For this purpose, a mixed integer programming model is developed and a heuristic algorithm based on Variable Neighborhood Search(VNS) is presented to solve the model. Finally, the computational results are presented and future research directions are discussed.

Keywords: location-routing problem, robust optimization, stochastic programming, variable neighborhood search

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Authors: Alper Akin, Ibrahim Aydogdu

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This study presents a hybrid metaheuristic algorithm to obtain optimum designs for steel space buildings. The optimum design problem of three-dimensional steel frames is mathematically formulated according to provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction). Design constraints such as the strength requirements of structural members, the displacement limitations, the inter-story drift and the other structural constraints are derived from LRFD-AISC specification. In this study, a hybrid algorithm by using teaching-learning based optimization (TLBO) and harmony search (HS) algorithms is employed to solve the stated optimum design problem. These algorithms are two of the recent additions to metaheuristic techniques of numerical optimization and have been an efficient tool for solving discrete programming problems. Using these two algorithms in collaboration creates a more powerful tool and mitigates each other’s weaknesses. To demonstrate the powerful performance of presented hybrid algorithm, the optimum design of a large scale steel building is presented and the results are compared to the previously obtained results available in the literature.

Keywords: optimum structural design, hybrid techniques, teaching-learning based optimization, harmony search algorithm, minimum weight, steel space frame

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Authors: P. Makendran, M. Pragadeesh, N. Narash, N. Manikandan, A. Rajasri, V. Sanal Kumar

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The growing severity of government-obligatory emissions legislation has required continuous improvement in catalysts performance and the associated reactor systems. IC engines emit a lot of harmful gases into the atmosphere. These gases are toxic in nature and a catalytic converter is used to convert these toxic gases into less harmful gases. The catalytic converter converts these gases by Oxidation and reduction reaction. Stoichiometric engines usually use the three-way catalyst (TWC) for simultaneously destroying all of the emissions. CO and NO react to form CO2 and N2 over one catalyst, and the remaining CO and HC are oxidized in a subsequent one. Literature review reveals that typically precious metals are used as a catalyst. The actual reactor is composed of a washcoated honeycomb-style substrate, with the catalyst being contained in the washcoat. The main disadvantage of a catalytic converter is that it exerts a back pressure to the exhaust gases while entering into them. The objective of this paper is to optimize the back pressure developed by the catalytic converter through geometric optimization of catalystic converter. This can be achieved by designing a catalyst with a optimum cone angle and a more surface area of the catalyst substrate. Additionally, the arrangement of the pores in the catalyst substrate can be changed. The numerical studies have been carried out using k-omega turbulence model with varying inlet angle of the catalytic converter and the length of the catalyst substrate. We observed that the geometry optimization is a meaningful objective for the lucrative design optimization of a catalytic converter for industrial applications.

Keywords: catalytic converter, emission control, reactor systems, substrate for emission control

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Authors: Asim Qureshi, R. S. Jangid

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Seismic response control of structures is generally achieved by using control devices which either dissipate the input energy or modify the dynamic properties of structure.In this paper, the response of a 20-storey benchmark building supplemented by viscous dampers and Negative Stiffness Device (NSD) is assessed by numerical simulations using the Newmark-beta method. True negative stiffness is an adaptive passive device which assists the motion unlike positive stiffness. The structure used in this study is subjected to four standard ground motions varying from moderate to severe, near fault to far-field earthquakes. The objective of the present study is to show the effectiveness of the adaptive negative stiffness device (NSD and passive dampers together) relative to passive dampers alone. This is done by comparing the responses of the above uncontrolled structure (i.e., without any device) with the structure having passive dampers only and also with the structure supplemented with adaptive negative stiffness device. Various performance indices, top floor displacement, top floor acceleration and inter-storey drifts are used as comparison parameters. It is found that NSD together with passive dampers is quite effective in reducing the response of aforementioned structure relative to structure without any device or passive dampers only. Base shear and acceleration is reduced significantly by incorporating NSD at the cost of increased inter-storey drifts which can be compensated using the passive dampers.

Keywords: adaptive negative stiffness device, apparent yielding, NSD, passive dampers

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Authors: Fatemeh Sadat Miri, Morteza Ehsani, Seyed Farshid Hosseini

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The present research is concerned with the optimization of extrusion parameters of ABS sheets by the Taguchi experimental design method. In this design method, three parameters of % recycling ABS, processing temperature and degassing time on mechanical properties, hardness, HDT, and color matching of ABS sheets were investigated. The variations of this research are the dosage of recycling ABS, processing temperature, and degassing time. According to experimental test data, the highest level of tensile strength and HDT belongs to the sample with 5% recycling ABS, processing temperature of 230°C, and degassing time of 3 hours. Additionally, the minimum level of MFI and color matching belongs to this sample, too. The present results are in good agreement with the Taguchi method. Based on the outcomes of the Taguchi design method, degassing time has the most effect on the mechanical properties of ABS sheets.

Keywords: ABS, process optimization, Taguchi, mechanical properties

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Authors: Ehsan Yadegari

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While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established.

Keywords: supply chain network design, teaching–learning-based optimization, improved metaheuristics, discrete solution representation

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Authors: Eduardo F. Mello, Peter H. Bauer

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The primary focus of this paper is the generation of energy-optimal speed trajectories for heterogeneous electric vehicle platoons in urban driving conditions. Optimal speed trajectories are generated for individual vehicles and for an entire platoon under the assumption that they can be executed without errors, as would be the case for self-driving vehicles. It is then shown that the optimization for the “average vehicle in the platoon” generates similar transportation energy savings to optimizing speed trajectories for each vehicle individually. The introduced approach only requires the lead vehicle to run the optimization software while the remaining vehicles are only required to have adaptive cruise control capability. The achieved energy savings are typically between 30% and 50% for stop-to-stop segments in cities. The prime motivation of urban platooning comes from the fact that urban platoons efficiently utilize the available space and the minimization of transportation energy in cities is important for many reasons, i.e., for environmental, power, and range considerations.

Keywords: electric vehicles, energy efficiency, optimization, platooning, self-driving vehicles, urban traffic

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Authors: Aziz Zoubir, Sefir Yamina, Djelti Redouan, Bentata Samir

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The effect of a uniform electric field across multibarrier systems (GaAs/AlxGa1-xAs) is exhaustively explored by a computational model using exact Airy function formalism and the transfer-matrix technique. In the case of biased Dimer Fibonacci Height Barrier superlattices (DFHBSL) structure a strong reduction in transmission properties was observed and the width of the miniband structure linearly decreases with the increase of the applied bias. This is due to the confinement of the states in the miniband structure, which becomes increasingly important (Wannier-Stark effect).

Keywords: Dimer Fibonacci Height Barrier superlattices, singular extended states, exact Airy function, transfer matrix formalism

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Authors: Mamidi Ramakrishna Rao, Jagdish Mamidi

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With advancing technology, the market environment for wind power generation systems has become highly competitive. The industry has been moving towards higher wind generator power ratings, in particular, off-shore generator ratings. Current off-shore wind turbine generators are in the power range of 10 to 12 MW. Unlike traditional induction motors, slow-speed permanent magnet surface mounted (PMSM) high-power generators are relatively challenging and designed differently. In this paper, PMSM generator design features have been discussed and analysed. The focus attention is on armature windings, harmonics, and permanent magnet. For the power ratings under consideration, the generator air-gap diameters are in the range of 8 to 10 meters, and active material weigh ~60 tons and above. Therefore, material weight becomes one of the critical parameters. Particle Swarm Optimization (PSO) technique is used for weight reduction and performance improvement. Four independent variables have been considered, which are air gap diameter, stack length, magnet thickness, and winding current density. To account for core and teeth saturation, preventing demagnetization effects due to short circuit armature currents, and maintaining minimum efficiency, suitable penalty functions have been applied. To check for performance satisfaction, a detailed analysis and 2D flux plotting are done for the optimized design.

Keywords: offshore wind generator, PMSM, PSO optimization, design optimization

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Authors: A. Mohammadi, M. Moghimi, S. Mohammadi

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Engineers need computational methods which could provide solutions less sensitive to the environmental effects, so the techniques should be used which take the uncertainty to account to control and minimize the risk associated with design and operation. In order to consider uncertainty in engineering problem, the optimization problem should be solved for a suitable range of the each uncertain input variable instead of just one estimated point. Using deterministic optimization problem, a large computational burden is required to consider every possible and probable combination of uncertain input variables. Several methods have been reported in the literature to deal with problems under uncertainty. In this paper, different methods presented and analyzed.

Keywords: uncertainty, Monte Carlo simulated, stochastic programming, scenario method

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Authors: Hamed Sarbazy, Zohre Gholipour Haftkhani, Ali Safari, Pejman Hosseiniun

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Scientific and industrial progress in the past two decades has resulted in energy distribution systems based on power electronics, as an enabling technology in various industries and building management systems can be considered. Grading and standardization module power electronics systems and its use in a distributed control system, a strategy for overcoming the limitations of using this system. The purpose of this paper is to investigate strategies for scheduling and control structure of standard modules is a power electronic systems. This paper introduces the classical control methods and disadvantages of these methods will be discussed, The hierarchical control as a mechanism for distributed control structure of the classification module explains. The different levels of control and communication between these levels are fully introduced. Also continue to standardize software distribution system control structure is discussed. Finally, as an example, the control structure will be presented in a DC distribution system.

Keywords: application management, hardware management, power electronics, building blocks

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Authors: Ouahab Kadri, Leila Hayet Mouss

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In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system.

Keywords: ant colony algorithms, complex and dynamic systems, diagnosis, classification, optimization

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Authors: Ashwini Gowda K. S, Archana M. R, Anjaneyappa V

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This paper presents pavement maintenance and management system (PMMS) to obtain optimum pavement maintenance and rehabilitation strategies and maintenance scheduling for a network using a multi-objective genetic algorithm (MOGA). Optimal pavement maintenance & rehabilitation strategy is to maximize the pavement condition index of the road section in a network with minimum maintenance and rehabilitation cost during the planning period. In this paper, NSGA-II is applied to perform maintenance optimization; this maintenance approach was expected to preserve and improve the existing condition of the highway network in a cost-effective way. The proposed PMMS is applied to a network that assessed pavement based on the pavement condition index (PCI). The minimum and maximum maintenance cost for a planning period of 20 years obtained from the non-dominated solution was found to be 5.190x10¹⁰ ₹ and 4.81x10¹⁰ ₹, respectively.

Keywords: genetic algorithm, maintenance and rehabilitation, optimization technique, pavement condition index

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Authors: Muhammet Emre Turan, Sait Özçelik, Yavuz Sun

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In railway rails, residual stress was measured and the values of residual stress were associated with hardness and micro structure in this study. At first, three rails as one meter long were taken and residual stresses were measured by cutting method according to the EN 13674-1 standardization. In this study, strain gauge that is an electrical apparatus was used. During the cutting, change in resistance in rail gave us residual stress value via computer program. After residual stress measurement, Brinell hardness distribution were performed for head parts of rails. Thus, the relationship between residual stress and hardness were established. In addition to that, micro structure analysis was carried out by optical microscope. The results show that, the micro structure and hardness value was changed with residual stress.

Keywords: residual stress, hardness, micro structure, rail, strain gauge

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Authors: Shirin Ghasemi

Abstract:

The structure of Asadi’s poem in Garshasb-Nameh coordinates with the structure of human psyche based on Jung’s perspective. The poetic stories of Asadi in Garshasb-Nameh is contrasted to human psyche according to Jung’s view in psychology which indicated the similarity of poetic structure of stories of Garshasb-Nameh to analytical psychology of Jung. In fact, by studying the stories of this collection the reader travels with him and finds it consistent with the human psyche. To demonstrate this, the story of Jamshid marriage with Kuhrang’s daughter and the story of Garshasb marriage with King’s daughter are selected. These two stories illustrate the poetic structure and the human psyche based on Jung’s analytical psychology perspective.

Keywords: Asadi Tusi, Garshasb-Nameh, Jung, analytical psychology

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Authors: Soudabeh Shemehsavar

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In this paper, we consider the situation under a life test, in which the failure time of the test units are not related deterministically to an observable stochastic time varying covariate. In such a case, the joint distribution of failure time and a marker value would be useful for modeling the step stress life test. The problem of accelerating such an experiment is considered as the main aim of this paper. We present a step stress accelerated model based on a bivariate Wiener process with one component as the latent (unobservable) degradation process, which determines the failure times and the other as a marker process, the degradation values of which are recorded at times of failure. Parametric inference based on the proposed model is discussed and the optimization procedure for obtaining the optimal time for changing the stress level is presented. The optimization criterion is to minimize the approximate variance of the maximum likelihood estimator of a percentile of the products’ lifetime distribution.

Keywords: bivariate normal, Fisher information matrix, inverse Gaussian distribution, Wiener process

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Authors: Hao Min Chuah, Tatsuya Yamazaki, Ryosui Iwasawa, Tatsumi Suto

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In recent years, with the advancement of digital technology, the movement to introduce so-called ICT (Information and Communication Technology), such as computer technology and network technology, to civil engineering construction sites and construction sites is accelerating. As part of this movement, attempts are being made in various situations to reproduce actual sites inside computers and use them for designing and construction planning, as well as for training inexperienced engineers. The installation of wave-dissipating blocks on coasts, etc., is a type of work that has been carried out by skilled workers based on their years of experience and is one of the tasks that is difficult for inexperienced workers to carry out on site. Wave-dissipating blocks are structures that are designed to protect coasts, beaches, and so on from erosion by reducing the energy of ocean waves. Wave-dissipating blocks usually weigh more than 1 t and are installed by being suspended by a crane, so it would be time-consuming and costly for inexperienced workers to train on-site. In this paper, therefore, a block installation simulator is developed based on Unity 3D, a game development engine. The simulator computes porosity. Porosity is defined as the ratio of the total volume of the wave breaker blocks inside the structure to the final shape of the ideal structure. Using the evaluation of porosity, the simulator can determine how well the user is able to install the blocks. The voxelization technique is used to calculate the porosity of the structure, simplifying the calculations. Other techniques, such as raycasting and box overlapping, are employed for accurate simulation. In the near future, the simulator will install an automatic block installation algorithm based on combinatorial optimization solutions and compare the user-demonstrated block installation and the appropriate installation solved by the algorithm.

Keywords: 3D simulator, porosity, user interface, voxelization, wave-dissipating blocks

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Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati

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In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.

Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network

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Authors: Luciano Roberto Fernandez-Sola, Cesar Augusto Arredondo-Velez, Miguel Angel Jaimes-Tellez

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This paper explores the modifications on peak acceleration, velocity and displacement profiles over the structure due to dynamic soil-structure interaction (DSSI). A shear beam model is used for the structure. Soil-foundation flexibility (inertial interaction) is considered by a set of springs and dashpots at the structure base. Kinematic interaction is considered using transfer functions. Impedance functions are computed using simplified expressions for rigid foundations. The research studies the influence of the slenderness ratio on the value of the peak floor response. It is shown that the modifications of peak floor responses are not the same for acceleration, velocity and displacement. This is opposite to the hypothesis used by methods included in several building codes. Results show that modifications produced by DSSI on different response quantities are not equal.

Keywords: peak floor intensities, dynamic soil-structure interaction, buildings with flexible base, kinematic and inertial interaction

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

Abstract:

In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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