@article { author = {Erzin, Yusuf and MolaAbasi, Hossein and Kordnaeij, Afshin and Erzin, Selin}, title = {Prediction of Compression Index of Saturated Clays Using Robust Optimization Model}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {4}, number = {3}, pages = {1-16}, year = {2020}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2020.233075.1226}, abstract = {Compression index (Cc) of normally consolidated (NC) clays determined by the oedometer experiments is utilized for calculating the consolidation settlement of shallow foundations. The determination of the Cc from the tests takes much more time and so empirical correlations based on clay properties can be a suitable alternative for the prediction of settlement. However, uncertainty in the measurements of input parameters has always been a major concern. Robust optimization is very popular due to its computational tractability for many classes of uncertainty sets and problem types. Therefore, in this research, an innovative method based on robust optimization has been used to investigate the effect of such uncertainties. To achieve these, the results of 433 oedometer tests taken from geotechnical investigation locations in Mazandaran province of Iran have been used. Based on Frobenius norm of the data points, uncertainty definition is presented and examined against the correlation coefficients for several empirical models for predicting Cc value and thus optimum values are determined. The results in compare with previous models indicate the robust method is a better pattern recognition tool for datasets with degrees of uncertainty. The variation of the Cc values with soil properties, namely, water content (ωn), initial void ratio (eo), and liquid limit (LL), by considering different value of uncertainties (0, 5 and 10%) was considered and indicated that the effect of eo is more than other two physical parameters (ωn and LL). The best model performance during in deterministic valuation and considering uncertainty is further shown.}, keywords = {Compression Index,Saturated Clays,Consolidation settlement,robust optimization,Second Order Cone}, url = {https://www.jsoftcivil.com/article_110937.html}, eprint = {https://www.jsoftcivil.com/article_110937_d3ae625448d5e19d427dde84b58618cc.pdf} } @article { author = {Harati, Mojtaba and Mashayekhi, Mohammadreza and Estekanchi, Homayoon}, title = {Correlation of Ground Motion Duration with Its Intensity Metrics: A Simulation Based Approach}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {4}, number = {3}, pages = {17-39}, year = {2020}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2020.227576.1207}, abstract = {There are different kinds of intensity measures to characterize the main properties of the earthquake records. This paper proposes a simulation-based approach to compute correlation coefficients of motion duration and intensity measures of the earthquake ground motions. This method is used to investigate the influence of the ground motion data set selection in resulting duration-intensity correlation coefficients. The simulation procedure is used to tackle the problem of inadequate available ground motions with specific parameters. Correlation coefficients are investigated in three different cases. In case one, simulated ground motions differ in terms of earthquake source parameters, site characteristics, and site-to-source distances. In case two, ground motions are simulated in a specific site from probable earthquake events. In case 3, ground motions are simulated from a specific event in different sites. The first case doesn’t show a significant correlation, while the second and the third case demonstrate significant positive and negative correlations, respectively.}, keywords = {Earthquake Ground Motion,Intensity Measure,strong ground motion duration,statistical correlation procedure}, url = {https://www.jsoftcivil.com/article_107854.html}, eprint = {https://www.jsoftcivil.com/article_107854_b931a1522fd761863185f05e329fc44e.pdf} } @article { author = {Sathya, Karunanithi and Sangavi, D. and Sridharshini, P. and Manobharathi, M. and Jayapriya, G.}, title = {Improved Image Based Super Resolution and Concrete Crack Prediction Using Pre-Trained Deep Learning Models}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {4}, number = {3}, pages = {40-51}, year = {2020}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2020.229355.1219}, abstract = {Detection and prediction of cracks play a vital role in the maintenance of concrete structures. The manual instructions result in having images captured from different sources wherein the acquisition of such images into the network may cause an error. The errors are rectified by a method to increase the resolution of those images and are imposed through Super-Resolution Generative Adversarial Network (SRGAN) with a pre-trained model of VGG19. After increasing the resolution then comes the prediction of crack from high resolution images through Convolutional Neural Network (CNN) with a pre-trained model of ResNet50 that trains a dataset of 40,000 images which consists of both crack and non-crack images. This work makes a comparative analysis of predicting the crack after and before the super-resolution method and their performance measure is compared. Compared with other methods on super-resolution and prediction, the proposed method appears to be more stable, faster and highly effective. For the dataset used in this work, the model yields an accuracy of 98.2%, proving the potential of using deep learning for concrete crack detection.}, keywords = {Generative adversarial network (GAN),Crack prediction,Super-resolution generative adversarial network (SRGAN),Highly resolution image,VGG19,ResNet50,Crack and non-crack images}, url = {https://www.jsoftcivil.com/article_110942.html}, eprint = {https://www.jsoftcivil.com/article_110942_6f33bbb572e3b83c2ca5dd4c01372d08.pdf} } @article { author = {Ajala, Abiodun and Adeyemo, Josiah and Akanmu, Semiu}, title = {The Need for Recurrent Learning Neural Network and Combine Pareto Differential Algorithm for Multi-Objective Optimization of Real Time Reservoir Operations}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {4}, number = {3}, pages = {52-64}, year = {2020}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2020.226578.1204}, abstract = {Reservoir operations need computational models that can attend to both its real time data analytics and multi-objective optimization. This is now increasingly necessary due to the growing complexities of reservoir’s hydrological structures, ever-increasing its operational data, and conflicting conditions in optimizing the its operations. Past related studies have mostly attended to either real time data analytics, or multi-objective optimization of reservoir operations. This review study, based on systematic literature analysis, presents the suitability of Recurrent Learning Neural Network (RLNN) and Combine Pareto Multi-objective Differential Evolution (CPMDE) algorithms for real time data analytics and multi-objective optimization of reservoir operations, respectively. It also presents the need for a hybrid RLNN-CPMDE, with the use of CPMDE in the development of RLNN learning data, for reservoir operation optimization in a multi-objective and real time environment. This review is necessary as a reference for researchers in multi-objective optimization and reservoir real time operations. The gaps in research reported in this review would be areas of further studies in real time multi-objective studies in reservoir operation.}, keywords = {Multi-Objective Optimization,reservoir operations,Real time recurrent learning Neural Network,Pareto,Differential evolution}, url = {https://www.jsoftcivil.com/article_110944.html}, eprint = {https://www.jsoftcivil.com/article_110944_7578923b4457306a305f35c90a57d5ad.pdf} } @article { author = {Ferdowsi, Ahmad and Hoseini, Seyed Mohamad and Farzin, Saeed and Faramarzpour, Mahtab and Mousavi, Sayed Farhad}, title = {Shape Optimization of Gravity Dams Using a Nature-Inspired Approach}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {4}, number = {3}, pages = {65-78}, year = {2020}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2020.224492.1196}, abstract = {In water infrastructures design problems, small changes in their geometries lead to a major variation in the construction time and costs. Dams are such important water infrastructures, which have different types regarding their materials and their behavior to endure loads. In the current paper, invasive weed optimization (IWO) algorithm is employed to find the best shape of a concrete gravity dam (Tilari Dam, India). Stress and stability were considered as design constraints, based on the following models: Model I (M1): upstream dam face is inclined and Model II (M2): upstream dam face is vertical. Optimization using IWO for M1 showed 20% reduction in cross-sectional area as compared to prototype. Although results obtained using IWO showed no changes in comparison with the algorithms in the literature (i.e., differential evolution, charged system search, colliding bodies optimization, and enhanced colliding bodies optimization), it converged faster. But results for M2 revealed 26% reduction in cross-sectional area.}, keywords = {Concrete Gravity Dams,Optimum Design,Nature-Inspired Algorithms,Invasive Weed Optimization (IWO) Algorithm,Shape Optimization}, url = {https://www.jsoftcivil.com/article_107850.html}, eprint = {https://www.jsoftcivil.com/article_107850_33353ae971388e14e7c3d66868c4ac1f.pdf} } @article { author = {Keerthi Gowda, B.S. and Easwara Prasad, G.L. and Velmurugan, R.}, title = {Prediction of Mechanical Strength Attributes of Coir/Sisal Polyester Natural Composites by ANN}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {4}, number = {3}, pages = {79-105}, year = {2020}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2020.226219.1200}, abstract = {Coir and Sisal are agriculture wastes that are effectively and financially accessible in the distinctive piece of Karnataka and other various states of republic India. These are generally treated as bio-compostable material by the customary horticulture/agriculture professionals. Aftereffects of past research related to fabrication, testing, analysis and design of conventional (synthetic fiber reinforced) composite materials portray that, strength to weight proportion is the basic criteria for a tailored design of composite materials. Viable utilizations of low-density reinforcing materials as the constituent materials of composites demonstrate great strength to weight ratio. Hence, 2 mm, 3 mm, 4 mm, 5 mm and 6 mm thick composite panels made up of 10 mm long coir/sisal fiber fortified in a polyester matrix of coupons are utilized for the experimentation process. The present study exhibits that the feed-forward Artificial Neural Network (ANN) model developed to predict the mechanical properties of coir/sisal polyester composite could be the acceptable mathematical tool for the prediction of mechanical properties of treated and untreated, arbitrarily oriented coir/sisal fiber strengthened polyester composite instead of the complicated experimental procedure. It exhibits that where traditional technique feels hard to estimate mechanical properties of coir/sisal fiber fortified polyester composite materials, the ANN model supports to foresee it. ANN approach avoids remembrance of equations and generalizes the problem domain and reduces the human error.}, keywords = {tensile strength,flexure strength,impact strength,Polymer matrix,plant fibers}, url = {https://www.jsoftcivil.com/article_110943.html}, eprint = {https://www.jsoftcivil.com/article_110943_ec84f5c94d9144a454eb9c96682cea19.pdf} } @article { author = {Adamu, Musa and Olalekan, Sani and Aliyu, Muhammad}, title = {Optimizing the Mechanical Properties of Pervious Concrete Containing Calcium Carbide and Rice Husk Ash Using Response Surface Methodology}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {4}, number = {3}, pages = {106-123}, year = {2020}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2020.229019.1216}, abstract = {Pervious Concrete (PC) have continued gaining acceptability significantly over years due to its sustainability and environmentally friendly. There are many benefits of using PC, some of which includes management of storm water runoff, groundwater supplier recharging and reduction of heat island effects etc. Numerous studies have been carried out through employing different approaches in order to improve the overall performance of PC. Due to the advancement in high performance PC using supplementary cementitious materials, extensive application of this material was made possible. In this study, calcium carbide waste (CCW) and rice husk ash (RHA) were used as supplementary cementitious materials and up to 20% replacement was made for both RHA and CCW in the PC mixes. Response surface methodology was used to derive the mathematical relationship between strengths and the variables RHA and CCW. The workability, flexural strength, compressive strength and splitting tensile strength were investigated and at 0%RHA and 10%CCW the strength of concrete increases significantly when compared with the control mix.}, keywords = {Pervious concrete,Rice Husk Ash,Calcium carbide waste,Compressive strength,tensile strength,Response Surface Methodology}, url = {https://www.jsoftcivil.com/article_112656.html}, eprint = {https://www.jsoftcivil.com/article_112656_416f33f8f1b416c6e26be87788230532.pdf} }