@article { author = {Gupta, Anjali and Biswas, Srijit and Arora, V.}, title = {Selection of Most Suitable Stabilized/Solidified Dredged Soil to Use in Highway Subgrade Layer Construction}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {3}, number = {1}, pages = {1-15}, year = {2019}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2018.146936.1089}, abstract = {The dredging of lakes, rivers, drains or water bodies, etc. is a regular practice all over the world and disposal of these dredged soils is a major problem due to the scarcity of open land in the urban areas. At present for handling this problem, engineers and soil experts are trying to find out alternative solutions such as the use of the dredged soil as constructional material in different development projects. This study deals with the contaminated dredged soil of Najafgarh drain, a major connecting drain of Yamuna river (Delhi) and aims at its use as alternative highway subgrade material after stabilization/solidification with cement, bottom ash and steel slag in different ratios. As the dredged soil contains a certain amount of organic matter that the influence the chemical process of stabilization/solidification, thus thermal treatment of raw dredged soil has also been carried out to ascertain its effects on stabilization/solidification. Furthermore, samples out of all those have satisfied the acceptance criteria of highway subgrade material have been selected, and finally, the most suitable sample out of them has been decided along with the assessment of its degree of suitability to use as highway subgrade materials. For both cases, the concept of the fuzzy logic of Prof. Latfi Zadeh has been introduced.}, keywords = {attributes,dredged soils,fuzzy decision,membership value,stabilization/solidification}, url = {https://www.jsoftcivil.com/article_82750.html}, eprint = {https://www.jsoftcivil.com/article_82750_866447e60a621861938b487641d38051.pdf} } @article { author = {Naderpour, Hosein and Mirrashid, Masoomeh}, title = {A Neuro-Fuzzy Model for Punching Shear Prediction of Slab-Column Connections Reinforced with FRP}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {3}, number = {1}, pages = {16-26}, year = {2019}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2018.136068.1073}, abstract = {In this article, one of the robust systems of soft computing namely adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the punching shear capacity of the concrete column-slab connections reinforced with FRP. For this purpose, a collection of experimental tests was used to train and test the ANFIS model. Five parameters including the area section of the column, Young’s modulus of the FRP bars, the effective flexural depth of the slab, FRP reinforcement ratio and also the compressive strength of concrete are used as inputs of the neuro-fuzzy system to estimate the considered output. The whole structure of the ANFIS also presented in mathematical steps. The obtained results of the created model of this paper indicated that the proposed ANFIS structure with a suitable accuracy could be used as a predictive model to determine the punching shear capacity of the considered elements. Also, the formulated model of the ANFIS in this paper can easily apply for codes and other researches.}, keywords = {Neuro-fuzzy system,FRP,Flat slab,Punching shear}, url = {https://www.jsoftcivil.com/article_82753.html}, eprint = {https://www.jsoftcivil.com/article_82753_7f6fc967f05cb98c51a2de56426933f2.pdf} } @article { author = {Wang, Jingmeng and Cui, Wenhua and Ye, Jun}, title = {Slope Stability Evaluation Using Tangent Similarity Measure of Fuzzy Cube Sets}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {3}, number = {1}, pages = {27-35}, year = {2019}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2019.176352.1100}, abstract = {Due to various geological problems and geological materials of the slope, there is a kind of non-continuous and uncertain natural geological body. Because of the complexity of various external factors, slope stability is not easy to be determined, which leads to the ambiguity of human’s judgments between stability and instability. Therefore, it is crucial that a simple evaluation method for judging the slope stability with uncertain information is established in slope stability analysis. This study selects nine impact factors: the lithology type, the slope structure, the development degree of discontinuity, the relationship between inclination and slope of discontinuities, the slope height, the slope angle, the mean annual precipitation, the weathering degree of rock, and the degree of human action, which can be expressed as the fuzzy cubic information (the hybrid information of both a fuzzy value and an interval-valued fuzzy number). Then, a tangent similarity measure between fuzzy cube sets (FCSs) is developed for the slope stability evaluation, where the tangent similarity measure values between FCSs of the slope sample and FCSs of slope stability grades/patterns (stability, slight stability, slight instability, and instability) are used for the assessment of the slope stability in FCS environment. Lastly, eight slope samples are provided as the actual cases to show that the eight evaluation results of slope stability using the proposed similarity measure of FCSs are in accordance with the actual results of the eight actual cases, which indicate the effectiveness of the proposed method under FCS environment.}, keywords = {slope stability,slope stability evaluation,fuzzy cube set,tangent similarity measure}, url = {https://www.jsoftcivil.com/article_87036.html}, eprint = {https://www.jsoftcivil.com/article_87036_1956857acbea88eade137cea6404b2e4.pdf} } @article { author = {Shahsavani, Hashem and Vafaei, Sahele and Mikaeil, Reza}, title = {Site Selection for Limestone Paper Plant Using AHP-Monte Carlo Approach}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {3}, number = {1}, pages = {36-46}, year = {2019}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2018.130134.1063}, abstract = {Paper played a crucial role in the history of the development of human society. Even in current times in the modern world, with Tablet, eBook readers and smart phones, the use of paper is still unavoidable. The wood needed for the production of the paper is provided by cutting down trees; hence, paper production has a cost to the environment. Recently, new technology has been developed which uses limestone instead of wood as the main material for paper production. This technology is environmentally friendly compared to the traditional paper-making technology. Choosing a suitable location for construction of such paper production plant based on different factors affecting paper quality is of great importance. To choose the desired location of such a plant, it is proposed to use a combination of Monte Carlo, and Analytical Hierarchic Process approaches. In this way, in the search area, there is a distribution of rates for each pixel instead of a single rate which allows determining the appropriate location for different confidence levels. The proposed method has been applied on Bijar, one of the cites of Kurdistan province in Iran, and a suitable location of the paper production plant is highlighted for various levels of confidence.}, keywords = {Paper plant,Limestone,Monte Carlo,AHP}, url = {https://www.jsoftcivil.com/article_76644.html}, eprint = {https://www.jsoftcivil.com/article_76644_6d1f5dab1c632b9a67d1d138b0e29811.pdf} } @article { author = {Dutta, Rakesh and Singh, Ajay and Gnananandarao, Tammineni}, title = {Prediction of Free Swell Index for the Expansive Soil Using Artificial Neural Networks}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {3}, number = {1}, pages = {47-62}, year = {2019}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2018.135575.1071}, abstract = {Prediction of the free swell index of the expansive soil using artificial neural network has been presented in this paper.  Input parameters for the artificial neural network model were plasticity index and shrinkage index, while the output was the free swell index. Artificial neural network algorithm used a back propagation model. Training of the artificial neural network model was conducted on the data collected from literature, and the weights and biases were obtained which described the relation among the input variables and the output free swell index. Further, the sensitivity analysis was performed, and the parameters affecting the free swell index of the expansive soil were identified. The sensitivity analysis results indicated that the plasticity index (63.97 %) followed by shrinkage index (36.03 %) was affecting the free swell index in this order. The study shows that the prediction accuracy of the free swell index of the expansive soil using artificial neural network model was quite good.}, keywords = {plasticity index,Shrinkage index,Free swell Index,expansive soil,Feed forward backpropagation algorithm,An artificial neural network,Multiple Regression Analysis}, url = {https://www.jsoftcivil.com/article_82862.html}, eprint = {https://www.jsoftcivil.com/article_82862_cb9712872781ebef97426bf47d080ad5.pdf} } @article { author = {Soltanpour Gharibdousti, Maryam and Azadeh, Ali}, title = {Performance Evaluation of Organizations Based on Human Factor Engineering Using Fuzzy Data Envelopment Analysis (FDEA)}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {3}, number = {1}, pages = {63-90}, year = {2019}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2019.177180.1101}, abstract = {With the growing rate of incidents at the workplace and the consequent increase of staff’s dissatisfaction, this study attempts to examine an integrated ergonomic system in a pharmaceutical company. In this study, the organization performance is assessed at different levels. First, an ergonomic questionnaire determines the most effective factors in the efficiency of the system by the means of fuzzy data envelopment analysis (FDEA). The best FDEA model is selected by making perturbation in the data and calculating the correlation between rankings. Then, a standard questionnaire is distributed among the customers and the most important factor in customer satisfaction is discovered. At last, suppliers are ranked based on the most important criteria using Hierarchical TOPSIS method. Next, the most influential factors managers and expert’s performance in health, safety and environment section are measured and strategies are proposed for performance improvement. The information obtained from performance evaluation can identify the worker's performance efficiency.}, keywords = {Macro-ergonomic,Micro-ergonomic,Organization Management,Customer Satisfaction,Human Factor Engineering,Fuzzy Data Envelopment Analysis,Hierarchal TOPSIS, Pharmaceutical company}, url = {https://www.jsoftcivil.com/article_89030.html}, eprint = {https://www.jsoftcivil.com/article_89030_5635adbe3d80eb9aced2edcf4f2ef1e2.pdf} } @article { author = {Chandanshive, Viren and Kambekar, Ajay}, title = {Estimation of Building Construction Cost Using Artificial Neural Networks}, journal = {Journal of Soft Computing in Civil Engineering}, volume = {3}, number = {1}, pages = {91-107}, year = {2019}, publisher = {Pouyan Press}, issn = {2588-2872}, eissn = {2588-2872}, doi = {10.22115/scce.2019.173862.1098}, abstract = {The cost estimation of the building construction projects at initial stages with a higher degree of accuracy plays a vital role in the success of every construction project. Based on the survey and feedback of the design professionals and construction contractors, a dataset of 78 building construction projects was obtained from a mega urban city Mumbai (India) and geographically nearby region. The most influential design parameters of the structural cost of buildings (Indian National Rupees: INR) were identified and assigned as an input and the total structural skeleton cost (INR) signifies the output of the neural network models. This research paper aims to develop a multilayer feed forward neural network model trained along with a backpropagation algorithm for the prediction of building construction cost (INR). The early stopping and Bayesian regularization approaches are implemented for the better generalization competency of neural networks as well as to avoid the overfitting. It has been observed during the construction cost prediction that the Bayesian regularization approach performance level is better than early stopping. The results obtained from the trained neural network model shows that it was able to predict the cost of building construction projects at the early stage of the construction. This study contributes to construction management and provides the idea about the entire financial budget that will be helpful for the property owners and financial investors in decision making and also to manage their investment in the volatile construction industry.}, keywords = {Artificial Neural Network,Cost Predictions,Early stopping,Regularization,Training Functions,Hidden Layers}, url = {https://www.jsoftcivil.com/article_89032.html}, eprint = {https://www.jsoftcivil.com/article_89032_9a2e28148ec5b773f259194d5709c658.pdf} }