Journal of Soft Computing in Civil EngineeringJournal of Soft Computing in Civil Engineering
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Feed provided by Journal of Soft Computing in Civil Engineering. Click to visit.Selection of Most Suitable Stabilized/Solidified Dredged Soil to Use in Highway Subgrade Layer ...
http://www.jsoftcivil.com/article_82750_9647.html
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 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 use of 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 certain amount of organic matter that 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. At the end, 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 assessment of its degree of suitability to use as highway subgrade materials. For both the cases, concept of fuzzy logic of Prof. Latfi Zadeh has been introduced.Mon, 31 Dec 2018 20:30:00 +0100Neural Network based model to Estimate Dynamic Modulus E* for mixtures in Costa Rica
http://www.jsoftcivil.com/article_89875_0.html
Various dynamic modulus (E*) predictive models have been developed to estimate E* as an alternative to laboratory testing. The most widely used model is the 1999 I-37A Witczak predictive equation based on North American mixtures laboratory results. The differences in material properties, traffic information, and environmental conditions for Latin American countries make it necessary to calibrate these models using local conditions. Consequently, the National Laboratory of Materials and Structural Models at the University of Costa Rica (in Spanish, LanammeUCR) has previously performed a local calibration of this model based on E* values for different types of Costa Rican mixtures. However, further research has shown that there is still room for improvement in the accuracy of the calibrated model (Witczak-Lanamme model) based on advanced regression techniques such as neural networks (NN). The objective of this study was to develop an improved and more effective dynamic modulus E* predictive regression model for mixtures in Costa Rica by means of NN based models. A comparison of the predicted E* values among the Witczak model, Witczak-Lanamme model and the new and improved model based on artificial neural networks (E* NN- model) indicated that the former not only met the model adequacy checking criteria but also exhibited the best goodness of fit parameters and the lowest overall bias. The findings of this study also supported the use of more advanced regression techniques that can become a more attractive alternative to local calibration of the Witczak I-37A equation.Mon, 01 Jul 2019 19:30:00 +0100A Neuro-Fuzzy Model for Punching Shear Prediction of Slab-Column Connections Reinforced with FRP
http://www.jsoftcivil.com/article_82753_9647.html
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.Mon, 31 Dec 2018 20:30:00 +0100Evolutionary algorithm performance evaluation in project time-cost optimization
http://www.jsoftcivil.com/article_89544_0.html
The time-cost trade-off problem pertains to the assessment of the best method of activity construction so that a project is completed within a given deadline and at least cost. Although several evolutionary-type of algorithms have been reported over the last two decades to solve this NP-hard combinatorial problem, there are not many comparative studies independently evaluating several methods. Such studies can provide support to project managers regarding the selection of the appropriate method. The objective of this work is to comparatively evaluate the performance potential of a number of evolutionary algorithms, each one with its own variations, for the time-cost trade-off problem. The evaluation is based on two measures of effectiveness, the solution quality (accuracy) and the processing time to obtain the solution. The solution is sought via a general purpose commercial optimization software without much interference in algorithm parameter setting and fine-tuning in an attempt to follow the anticipated project manager approach. The investigation has been based on case studies from the literature with varying project size and characteristics. Results indicate that certain structures of genetic algorithms, particle swarm optimization, and differential evolution method present the best performance.Sun, 23 Jun 2019 19:30:00 +0100Slope Stability Evaluation Using Tangent Similarity Measure of Fuzzy Cube Sets
http://www.jsoftcivil.com/article_87036_9647.html
Due to various geological problems and geological materials of slope, there is a kind of non-continuous and uncertain natural geological body. Because of the complexity of various external factors, the 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.Mon, 31 Dec 2018 20:30:00 +0100Application of Soft Computing Techniques in Predicting the Ultimate Bearing Capacity of Strip ...
http://www.jsoftcivil.com/article_89749_0.html
The present study attempts to predict the ultimate bearing capacity (UBC) of the strip footing resting on sand and subjected to inclined load having eccentricity with respect to the vertical using three different soft computing techniques such as support vector mechanism with radial basis function (SVM RBF kernel), M5P model tree (M5P) and random forest regression (RFR). The UBC was computed in the form of reduction factor and this reduction factor was assumed to be dependent on the ultimate bearing capacity (qu) of the strip footing subjected to vertical load, eccentricity ratio (e/B), inclination ratio (α/ϕ) and the embedment ratio (Df/B). The performance of each model was analyzed by comparing the statistical performance measure parameters. The outcome of present study suggests that SVM RBF kernel predicts the reduction factor with least error followed by M5P and RFR. All the model predictions further outperformed those based on semi-empirical approach available in literature. Finally, sensitivity analysis performed for the SVM RBF kernel model which suggests that the inclination ratio (α/ϕ) and eccentricity ratio (e/B) was an important parameter, in comparison to other parameters, considered for predicting the reduction factor.Thu, 27 Jun 2019 19:30:00 +0100Site selection for limestone paper plant using AHP-Monte Carlo approach
http://www.jsoftcivil.com/article_76644_9647.html
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 environment. Recently, a 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 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 to determine 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 paper production plant is highlighted for various levels of confidence.Mon, 31 Dec 2018 20:30:00 +0100Prediction of Free Swell Index for the Expansive Soil Using Artificial Neural Networks
http://www.jsoftcivil.com/article_82862_9647.html
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.Mon, 31 Dec 2018 20:30:00 +0100Application of random forest regression in the Prediction of ultimate bearing capacity of strip ...
http://www.jsoftcivil.com/article_89975_0.html
The paper presents the prediction of the ultimate bearing capacity of the strip footing resting on dense sand overlying loose sand deposit using random forest regression. In this study, 181 data collected from literature were used. 71 % of the total data was randomly selected for training the model and the rest of the data were utilized for the testing purpose. The various input parameters were friction angle of the dense sand layer (1), friction angle of the loose sand layer (2), unit weight of the dense sand layer (1), unit weight of the loose sand layer (), ratio of the thickness of the dense sand layer below base of the footing to the width of footing (H/B), ratio of the depth of the footing to the width of the footing (D/B) and (H+D)/B. Ultimate bearing capacity was the output in this study. Performance measures were used in order to make the comparison with the artificial neural network and M5P model tree. The result of this study reveals that the performance of the random forest regression was superior to the other soft computing techniques. The results of the sensitivity analysis reveals that the unit weight and the friction angle of the loose sand layer were the most important parameters affecting the output ultimate bearing capacity of the strip footing resting on the layered sand deposit.Thu, 04 Jul 2019 19:30:00 +0100Performance Evaluation of Organizations based on Human Factor Engineering Using FDEA
http://www.jsoftcivil.com/article_89030_9647.html
With the growing rate of incidents at workplace and 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 got from performance evaluation can identify the workers performance efficiency.Mon, 31 Dec 2018 20:30:00 +0100Estimation of building construction cost using artificial neural networks
http://www.jsoftcivil.com/article_89032_9647.html
The cost estimation with higher degree of accuracy at initial stage of the building construction projects plays a vital role in the success of every construction projects. Based on a survey of design professionals and contractors, dataset of 78 buildings construction projects was obtained from urban city of Mumbai, India and nearby region. The most influential design parameters of the structural cost of buildings were identified and assigned as an input and the total structural skeleton cost signifies the output of the neural network models. This paper discusses the development of a multilayer feed-forward neural network model trained with a back propagation algorithm for the prediction of building construction cost. The early stopping and Bayesian regularization approaches are implemented for the better generalization competency of neural networks and to avoid the over fitting. The Bayesian regularization approach performance level during the construction cost prediction is better than early stopping. The results obtained from the trained neural network model shows that, the neural network is able to predict the cost of building construction projects at the early stage of the construction. This paper contributes to construction management and provides the idea about the entire outlay budget which will be helpful to the owners and investors in decision making and to manage their investment.Mon, 24 Jun 2019 19:30:00 +0100