2024-03-29T06:37:46Z
https://www.jsoftcivil.com/?_action=export&rf=summon&issue=9636
Journal of Soft Computing in Civil Engineering
J. Soft Comput. Civ. Eng.
2018
2
4
Developing Four Metaheuristic Algorithms for Multiple-Objective Management of Groundwater
Hamdy
El-Ghandour
Emad
Elbeltagi
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.
Genetic Algorithms
Memetic algorithms
Particle swarm
Shuffled frog leaping
Compromise solution
Multiple objectives optimization
2018
10
01
1
22
https://www.jsoftcivil.com/article_64764_ef703d1195151f57ae64cd2e327790b5.pdf
Journal of Soft Computing in Civil Engineering
J. Soft Comput. Civ. Eng.
2018
2
4
Optimum Design of Structures for Seismic Loading by Simulated Annealing Using Wavelet Transform
Ali
Heidari
Jalil
Raeisi
Optimization of earthquake-affected structures is one of the most widely used methods in structural engineering. In this paper optimum design of structures for earthquake loading was achieved by simulated annealing method. The evolutionary algorithm was employed for optimum design of two space structures. To reduce the computational work, a discrete wavelet transform (DWT) was used. In DWT the number of points in the earthquake record was decreased with Mallat Method. A dynamic analysis of time history was carried out. By DWT the earthquake signal was decomposed into a number of points. Then the two space structures were optimized for these reduce points. The actual responses were reconstructed with a reverse wavelet transform (RWT). A number of space structures were designed for minimum weight. The result show, DWT and RWT were an effective approach for reducing the computational cost of optimization.
Simulated Annealing
Discrete Wavelet transform
Reverse Wavelet Transform
Dynamic analysis
2018
10
01
23
33
https://www.jsoftcivil.com/article_62820_ea0c8a7bf98d8ecfe2ff665cc9a85b96.pdf
Journal of Soft Computing in Civil Engineering
J. Soft Comput. Civ. Eng.
2018
2
4
Prediction of Ultimate Bearing Capacity of Skirted Footing Resting on Sand Using Artificial Neural Networks
Rakesh
Dutta
Radha
Rani
Tammineni
Gnananandarao
The paper presents the prediction of ultimate bearing capacity of different regular shaped skirted footing resting on sand using 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 sand were identified. The study shows that the prediction accuracy of ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network model was quite good.
Different regular shaped skirted footings
Ultimate bearing capacity
Feed forward backpropagation algorithm
Artificial Neural Network
Multiple Regression Analysis
2018
10
01
34
46
https://www.jsoftcivil.com/article_64822_73a6d72d8223f499bbd1f90720f72f51.pdf
Journal of Soft Computing in Civil Engineering
J. Soft Comput. Civ. Eng.
2018
2
4
Prediction of Concrete and Steel Materials Contained by Cantilever Retaining Wall by Modeling the Artificial Neural Networks
Umit
Gokkus
Mehmet
Yildirim
Arif
Yilmazoglu
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). 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). 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.
Inverse T-Shaped Retaining Walls
Stem-Stepped Walls
Concrete volume and steel area in wall design
Prediction with neural network
Artificial Bee Colony-Based Preliminary Wall Design
2018
10
01
47
61
https://www.jsoftcivil.com/article_65561_1a56693825333de4db34095b73c5d815.pdf
Journal of Soft Computing in Civil Engineering
J. Soft Comput. Civ. Eng.
2018
2
4
Scale Effect and Anisotropic Analysis of Rock Joint Roughness Coefficient Neutrosophic Interval Statistical Numbers Based on Neutrosophic Statistics
Wenzhong
Jiang
Jun
Ye
Wenhua
Cui
In rock mechanics, mechanical properties of rock masses in nature imply complexity and diversity. The shear strength of rock mass is a key factor for affecting the stability of the rock mass. Then, the joint roughness coefficient (JRC) of rock indicates an important parameter in the shear strength and stability analysis of rock mass. Since the nature of the rock mass is indeterminate and incomplete to some extent, we cannot always express rock JRC by a certain/exact number. Therefore, this paper introduces neutrosophic interval statistical numbers (NISNs) based on the concepts of neutrosophic numbers and neutrosophic interval probability to express JRC data of the rock mass in the indeterminate setting. Then we present the calculational method of the neutrosophic average value and standard deviation of NISNs based on neutrosophic statistics. Next, by an actual case, the neutrosophic average value and standard deviation of the rock JRC NISNs are used to analyze the scale effect and anisotropy of the rock body corresponding to different sample lengths and measuring directions. Lastly, the analysis method of the scale effect and anisotropy for JRC NISNs shows its effectiveness and rationality in the actual case study.
Joint roughness coefficient (JRC)
Neutrosophic interval probability
Neutrosophic interval statistical number
Neutrosophic statistics
Scale effect
Anisotropy
2018
10
01
62
71
https://www.jsoftcivil.com/article_81314_5f8a68c82cd53adddca5332b93f48762.pdf
Journal of Soft Computing in Civil Engineering
J. Soft Comput. Civ. Eng.
2018
2
4
Application of Adaptive Neuro-Fuzzy Inference System to Estimate Alongshore Sediment Transport Rate (A Real Case Study: Southern Shorelines of Caspian Sea)
Tayeb
Sadeghifar
Reza
Barati
Many empirical models have been introduced by scientists during the recent decades for estimating longshore sediment transport rate, but these approaches have been calibrated and applied under limited conditions of the bed profile and specific range of the bed sediment size. The existing empirical relations are linear or exponential regressions based on the observation and measurements data, and there’s a great potential to build more accurate models to predict sediment transport phenomena using soft computation approach. This paper presents a novel case study application of the adaptive Neuro-fuzzy inference system (ANFIS) as a superior modeling technique for estimation of the longshore sediment transport rate in the southern shorelines of the Caspian Sea. The results will be compared with the top three popular existing empirical equations. Daily grab samples from four stations were collected from March 2012 through June 2012. The trained ANFIS model outperformed the existing regression-type empirical equations for the estimation of the alongshore sediment transport rate due to the adaptive structure of the ANFIS model to better fit complex systems.
Alongshore sediment transports rate
Semi-empirical formula
adaptive neuro-fuzzy inference system
Caspian Sea
Noor coastal
2018
10
01
72
85
https://www.jsoftcivil.com/article_81315_542b82dc9f4bff7e1b63d9bbee0d9792.pdf
Journal of Soft Computing in Civil Engineering
J. Soft Comput. Civ. Eng.
2018
2
4
An Equation to Determine the Ultimate Flexural Load of RC Beams Strengthened with CFRP Laminates
Reza
Farahnaki
Alla
Azimi
In this paper, a new relationship is presented for determining the ultimate flexural load of reinforced concrete beams strengthened with CFRP laminates. An artificial neural network with a suitable performance was used to estimate this equation. First, a collection of laboratory results including 83 data was collected from valid references. This database was then divided into three groups of 51, 16, and 16, which were used to train, validation, and test the proposed equation, respectively. The final model had eleven inputs including concrete compressive strength, width of beam, effective depth, area of tension reinforcement, area of compression reinforcement, yield strength of steel, modulus of elasticity of steel, modulus of elasticity of CFRP sheet, width of CFRP sheet, total thickness of CFRP sheets and, length of CFRP sheet, which were applied to the network to determine the ultimate flexural load as the output of the model. The obtained results from the proposed relationship showed that it was able to use as a predictive equation for the considered target.
concrete beam
Flexural load
FRP
strengthening
2018
10
01
86
95
https://www.jsoftcivil.com/article_76643_8fc1d578e3345acfa1f8cb7371a5f6c3.pdf