Soft Computing in Civil EngineeringSoft Computing in Civil Engineering
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Sat, 22 Sep 2018 14:41:23 +0100FeedCreatorSoft Computing in Civil Engineering
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Feed provided by Soft Computing in Civil Engineering. Click to visit.Refined Simplified Neutrosophic Similarity Measures Based on Trigonometric Function and Their ...
http://www.jsoftcivil.com/article_61655_5935.html
Refined simplified neutrosophic sets (RSNSs) are appropriately used in decision-making problems with sub-attributes considering their truth components, indeterminacy components and falsity components independently. This paper presents the similarity measures of RSNSs based on tangent and cotangent functions. When the weights of each element/attribute and each sub-element/sub-attribute in RSNSs are considered according to their importance, we propose the weighted similarity measures of RSNSs and their multiple attribute decision-making (MADM) method with RSNS information. In the MADM process, the developed method gives the ranking order and the best selection of alternatives by getting the weighted similarity measure values between alternatives and the ideal solution according to the given attribute weights and sub-attribute weights. Then, an illustrative MADM example in a construction project with RSNS information is presented to show the effectiveness and feasibility of the proposed MADM method under RSNS environments. This study extends existing methods and provides a new way for the refined simplified neutrosophic MADM problems containing both the attribute weight and the sub-attribute weights.Sat, 30 Jun 2018 19:30:00 +0100Developing Four Metaheuristic Algorithms for Multiple-Objective Management of Groundwater
http://www.jsoftcivil.com/article_64764_0.html
Groundwater is one of the important sources of freshwater and accordingly, there is a need for optimizing its usage. In this paper, four multi-objective metaheuristic algorithms with new evolution strategy are introduced and compared for the optimal management of groundwater namely: Multi-objective genetic algorithms (MOGA), multi-objective memetic algorithms (MOMA), multi-objective particle swarm optimization (MOPSO), and multi-objective shuffled frog leaping algorithm (MOSFLA). The suggested evolution process is based on determining a unique solution of the Pareto solutions called the Pareto-compromise (PC) solution. The advantages of the current development stem from: 1) The new multiple objectives evolution strategy is inspired from the single objective optimization, where fitness calculations depend on tracking the PC solution only through the search history; 2) a comparison among the performance of the four algorithms is introduced. The development of each algorithm is briefly presented. A comparison study is carried out among the formulation and the results of the four algorithms. The developed four algorithms are tested on two multiple-objective optimization benchmark problems. The four algorithms are then used to optimize two-objective groundwater management problem. The results prove the ability of the developed algorithms to accurately find the Pareto-optimal solutions and thus the potential application on real-life groundwater management problems.Tue, 19 Jun 2018 19:30:00 +0100Connectivity and Flowrate Estimation of Discrete Fracture Network Using Artificial Neural Network
http://www.jsoftcivil.com/article_59741_5935.html
Hydraulic parameters of rock mass are the most effective factors that affect rock mass behavioral and mechanical analysis. Aforementioned parameters include intensity and density of fracture intersections, percolation frequency, conductance parameter and mean outflow flowrate which flowing perpendicular to the hydraulic gradient direction. In order to obtain hydraulic parameters, three-dimensional discrete fracture network generator, 3DFAM, was developed. But unfortunately, hydraulic parameters obtaining process using conventional discrete fracture network calculation is either time consuming and tedious. For this reason, in this paper using Artificial Neural Network, a tool is designed which precisely and accurately estimate hydraulic parameters of discrete fracture network. Performance of designed optimum artificial neural network is evaluated from mean Squared error, errors histogram, and correlation between artificial neural network predicted value and with discrete fracture network conventionally calculated value. Results indicate that there is acceptable value of mean squared error and also major part of estimated values deviation from actual value placed in acceptable error interval of (-1.17, 0.85). In the other hand, excellent correlation of 0.98 exist between predicted and actual value that prove reliability of designed artificial neural network.Sat, 30 Jun 2018 19:30:00 +0100Optimum Design of Structures Against earthquake by Simulated Annealing Using Wavelet Transform
http://www.jsoftcivil.com/article_62820_0.html
Optimization of earthquake-affected structures is one of the most widely used methods in structural engineering. In this paper optimum design of structures is achieved by simulated annealing method. The evolutionary algorithm is employed for optimum design of structures. To reduce the computational work, a discrete wavelet transform is used by means of which the number of points in the earthquake record is decreased. The loads are considered as earthquake loads. A time history analysis is carried out for the dynamic analysis. By discrete wavelet transform (DWT) the earthquake record is decomposed into a number of points. Then in the optimization process, the structures are analyzed for these points. To reconstruct the actual responses from these points, a reverse wavelet transform (RWT) was used. A number of space structures are designed for minimum weight and the results are compared with exact dynamic analysis. The result show, DWT and RWT were an effective approach for reducing the computational cost of optimization.Tue, 22 May 2018 19:30:00 +0100Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network
http://www.jsoftcivil.com/article_59743_5935.html
The selection of appropriate type and grade of concrete for a particular application is the critical step in any construction project. Workability & compressive strength are the two significant parameters that need special attention. The aim of this study is to predict the slump along with 7-days & 28-days compressive strength based on the data collected from various RMC plants. There are many studies reported in general to address this issue time to time over a long period. However, considering the worldwide use of a huge quantity of concrete for various infrastructure projects, there is a scope for the study that leads to most accurate estimate. Here, data from various concrete mixing plants and ongoing construction sites was collected for M20, M25, M30, M35, M40, M45, M50, M55, M60 and M70 grade of concrete. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were built to predict slump as well as 7-days and 28-days compressive strength. A variety of experiments was carried out that suggests ANN performs better and yields more accurate prediction compared to MLR model for both slump & compressive strength.Sat, 30 Jun 2018 19:30:00 +0100Prediction of Ultimate Bearing Capacity of Skirted Footing Resting on Sand Using Artificial ...
http://www.jsoftcivil.com/article_64822_0.html
The paper presents the prediction of the ultimate bearing capacity of different regular shaped skirted footing resting on sand using the artificial neural network. The input parameters for the artificial neural network model were normalised skirt depth, area of the footing and the friction angle of the sand, while the output was the ultimate bearing capacity. The artificial neural network algorithm uses a back propagation model. The training of artificial neural network model has been conducted and the weights were obtained which described the relationship between the input parameters and output ultimate bearing capacity. Further, the sensitivity analysis has been performed and the parameters affecting the ultimate bearing capacity of different regular shaped skirted footing resting on the sand were identified. The study shows that the prediction accuracy of the ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network model was quite good.Thu, 21 Jun 2018 19:30:00 +0100Application of ANN in Estimating Discharge Coefficient of Circular Piano Key Spillways
http://www.jsoftcivil.com/article_60611_5935.html
Among all solutions for disrupted vortex formation in shaft spillways, an innovative one called Circular Piano Key Spillway, based upon piano key weir principles, has been experimented less. In this study, the potential of Artificial Neural Networks (ANN) in estimating the amounts of discharge coefficient of Circular Piano Key Spillway has been evaluated. In order to pursue this purpose, the results of some physical experiments were used. These experiments have been conducted in the hydraulic laboratory using different physical models of Circular Piano Key Spillway including three models with different angles of 45, 60 and 90 degrees. Data from those experiments were used in training and test steps of ANN models. Multilayer Perceptron (MLP) network with Levenberg-Marquardt backpropagation algorithm was used. The performance of artificial neural network was measured by these statistical indicators: coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) and optimum quantities of statistical indicators for test step were assessed 0.9999, 0.4988, 0.5963 and 0.9999 respectively, for Circular Piano Key Spillway with an angle of 90 degree and for training step were assessed 0.9999, 0.5479, 0.6305 and 0.9999 respectively, for Circular Piano Key Spillway with an angle of 90 degree. In other words, Circular Piano Key Spillway with an angle of 90 degrees has the optimum performance, both in training and test steps. Artificial Neural Network model can successfully estimate the amounts of discharge coefficient of Circular Piano Key Spillway.Sat, 30 Jun 2018 19:30:00 +0100Prediction of Concrete and Steel Materials Contained by Cantilever Retaining Wall by Modeling ...
http://www.jsoftcivil.com/article_65561_0.html
In this study, the Artificial Neural Network (ANN) application is implemented for predicting the required concrete volume and amount of the steel reinforcement within the inversed-T-shaped and stem-stepped reinforced concrete (RC) walls. For this aim, seven-different RC wall designs were approached differentiated within the wall heights and various internal friction angles of backfill materials. Each RC wall is proportionally designed and subjected to active lateral earth pressure defined with the Mononobe-Okabe approach foreseen by Turkish Specification for Building to be Built in Seismic Zones (TSC-2007)[14]. Following the stability analysis of the RC retaining walls, the structural and reinforced concrete analyses are performed according to the Turkish Standard on Requirements for Design and Construction in Reinforced Concrete Structures (TS500-2000)[15]. Input parameters such as concrete volumes, weights of the steel bars, soil and wall material properties are subjected to the ANN modeling. The prediction of the concrete volume and amount of the steel bars are achieved with the implementation of the ANN model trained with the Artificial Bee Colony (ABC) algorithm. As a result of this study, it is revealed that ANN models are useful for verifying the existing RC retaining wall designs or performing preliminary designs for the L-shaped and stem-stepped cantilever retaining walls.Tue, 10 Jul 2018 19:30:00 +0100Artificial neural networks prediction of compaction characteristics of black cotton soil ...
http://www.jsoftcivil.com/article_63018_5935.html
Artificial neural networks (ANNs) that has been successfully applied to structural and most other disciplines of civil engineering is yet to be extended to soil stabilization aspect of geotechnical engineering. As such, this study aimed at applying the ANNs as a soft computing approach that were trained with the feed forward back-propagation algorithm, for the simulation of optimum moisture content (OMC) and maximum dry density (MDD) of cement kiln dust-stabilized black cotton soil. Ten input and two output data set were used for the ANN model development. The mean squared error (MSE) and R-value were used as yardstick and criterions for acceptability of performance. In the neural network development, NN 10-5-1 and NN 10-7-1 respectively for OMC and MDD that gave the lowest MSE value and the highest R-value were used in the hidden layer of the networks architecture and performed satisfactorily. For the normalized data used in training, testing and validating the neural network, the performance of the simulated network was satisfactory having R values of 0.983 and 0.9884 for the OMC and MDD, respectively. These values met the minimum criteria of 0.8 conventionally recommended for strong correlation condition. All the obtained simulation results are satisfactory and a strong correlation was observed between the experimental OMC and MDD values as obtained by laboratory tests and the predicted values using ANN.Sat, 30 Jun 2018 19:30:00 +0100Process Parameter Optimization for minimizing Springback in Cold Drawing Process of Seamless ...
http://www.jsoftcivil.com/article_63664_5935.html
In tube drawing process, a tube is pulled out through a die and a plug to reduce its diameter and thickness as per the requirement. Dimensional accuracy of cold drawn tubes plays a vital role in further quality of end products and controlling rejection in manufacturing processes of these end products. Springback phenomenon is the elastic strain recovery after removal of forming loads, causes geometrical inaccuracies in drawn tubes. Further this leads to difficulty in achieving close dimensional tolerances. In the present work springback of EN 8 D tube material is studied for various cold drawing parameters. The process parameters in this work include die semi angle, land width and drawing speed. The experimentation is done using Taguchi’s L36 orthogonal array and then optimization is done in data analysis software Minitab 17.The results of ANOVA shows that 15 degree die semi angle,5 mm land width and 6 m/min drawing speed yields least springback. Furthermore, optimization algorithms named Particle Swarm Optimization (PSO),Simulated Annealing (SA) and Genetic Algorithm (GA) are applied which shows that 15 degree die semi angle, 10 mm land width and 8 m/min drawing speed results in minimal springback with almost 10.5 % improvement. Finally the results of experimentation are validated with Finite Element Analysis technique using ANSYS.Sat, 30 Jun 2018 19:30:00 +0100The Gaussian process modelling module in UQLab
http://www.jsoftcivil.com/article_64721_5935.html
We introduce the Gaussian process (GP) modelling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modelling into any uncertainty quantification workflow, as well as a standalone surrogate modelling tool. We first briefly present the key mathematical tools at the basis of GP modelling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and user-friendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a well-known analytical benchmark function, a hierarchical Kriging example applied to wind turbine aero-servo-elastic simulations and a more complex geotechnical example that requires a non-stationary, user-defined correlation function. The GP-module, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).Sat, 30 Jun 2018 19:30:00 +0100