Pouyan PressJournal of Soft Computing in Civil Engineering2588-28721220171001The Application of Particle Swarm Optimization and Artificial Neural Networks to Estimating the Strength of Reinforced Concrete Flexural Members174844310.22115/scce.2017.48443ENReza FarahnakiUniversity of Wollongong, New South Wales, AustraliaJournal Article20170702The aim of this paper is a determination of the shear strength of fiber reinforced polymer reinforced concrete flexural members without stirrups. For this purpose, a neural network approach was used. The weights and biases of the considered network determined based on best values which were optimized from the particle swarm optimization algorithm (PSO). For training the model, a collection of 108 datasets which was published in literature was applied. Six inputs including the compressive strength of concrete, flexural FRP reinforcement ratio, modulus of elasticity for FRP, shear span-to-depth ratio, member web width and adequate member depth used for creating the model while the shear strength considered as the output. The best structure for the network was obtained by a network with one hidden layer and ten nodes. The results indicated that artificial neural networks based on particle swarm optimization algorithm could be able to predict the strength of the considered RC elements.https://www.jsoftcivil.com/article_48443_5db4c6d15da54f7a1e8c59efe66b1585.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28721220171001Calculation of Torsion Capacity of the Reinforced Concrete Beams Using Artificial Neural Network8184868510.22115/scce.2017.48685ENMohammad Hosein IlkhaniPh.D. Candidate, Faculty of Civil Engineering, Semnan University, Semnan, IranEhsan MoradiPh.D. Candidate, Faculty of Civil Engineering, Semnan University, Semnan, IranMohammad LavasaniPh.D., Florida International University, Miami, FL, USAJournal Article20170613This paper presents a model for calculation of torsion capacity of the reinforced concrete beams using the artificial neural network. Considering the complex reaction of reinforced concrete beams under torsion moments, torsion strength of these beams is depended on different parameters; therefore using the artificial neural network is a proper method for estimating the torsion capacity of the beams. In the presented model the beam's dimensions, concrete compressive strength and longitudinal and traverse bars properties are the input data, and torsion capacity of the reinforced concrete beam is the output of the model. Also considering the neural network results, a sensitivity analysis is performed on the network layers weight, and the effect of different parameters is evaluated on the torsion strength of the reinforced concrete beams. According to the sensitivity analysis, properties of traverse steel have the most effect on torsion capacity of the beams.https://www.jsoftcivil.com/article_48685_7c8e3840821038bd09162762d3bb6f3d.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28721220171001Optimisation of Recycled Thermoplastic Plate (Tile)19344897210.22115/scce.2017.48972ENLonping TazouOlivierDepartment of Civil Engineering, University of Ilorin, Ilorin, NigeriaAlao AbdullahiJimohDepartment of Civil Engineering, University of Ilorin, Ilorin, NigeriaAdeola A.AdedejiDepartment of Civil Engineering, University of Ilorin, Ilorin, NigeriaJournal Article20170712The purpose of this paper is to perform a structural optimization of a flat thermoplastic plate (tile). This task is developed computationally through the interface between an optimization algorithm and the finite element method with the goal of minimizing the equivalent stress with specified target stress of 2 MPa when applied with a load intensity of 1000N. A 300 x 300 x 20 mm thermoplastic plate was selected for the optimization, which was performed with a tool in MATLAB R2012b known as genetic algorithm accompanied with static analysis in ANSYS 15. The results produced the optimum equivalent stress (δ<sub>opt</sub>) of 2.136 MPa with the optimum dimensions of 305 x 302 x 20 mm. Also, the dimensions of the plate with the optimum value of the equivalent stress were discovered to be within the lower and upper bound dimensions of the plate. The thermoplastic plate object of the optimization was a square plate of 300 x 300mm, and 20 mm thick with isotropic properties and a particular load and boundary conditions were applied on the entire plate.https://www.jsoftcivil.com/article_48972_0ab360e3c1840cd65f7b2efa85fc335a.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28721220171001Seismic Analysis and Design of a Multi-Storey Building Located in Haql City, KSA35514908310.22115/scce.2017.49083ENMohammed IsmaeilAssistant Professor, Department of Civil Engineering, King Khalid University, KSA. On leave from Sudan University for Science and Technology, Khartoum, SudanKhalid ElhadiAssistant Professor, Department of Civil Engineering, King Khalid University, KSA. On leave from Structural Engineering Department, Zagazig University, Zagazige, EgyptYasser AlashkerAssistant Professor, Department of Civil Engineering, King Khalid University, KSA. On leave from Structural Engineering Department, Zagazig University, Zagazige, EgyptIsam Eldin YousefLecturer, Department of Civil Engineering, King Khalid University, KSAJournal Article20170729Recently the design of RC building to mitigate seismic loads has received great attention. Since Saudi Arabia has low to moderate seismicity, most of the buildings were designed only for gravity load. The objective of this paper is to analysis design RC building located in the most active seismic zone region in Saudi Arabia to mitigate seismic loads. A multi-story reinforced concrete building, in Haql city, was seismically analyzed and designed using the Equivalent Lateral Force Procedure with the aid of SAP200 software. The chosen buildings which were Ordinary Moment Resisting Frame (OMR), was analyzed and designed by using SBC 301 (2007) Saudi Building Code [1], SAP2000 (structural analysis software) [2] and ISACOL "Information Systems Application on Reinforced Concrete Columns" [3]. The results showed that the current design of RC buildings located in the most active seismic zone region in Saudi Arabia, Haql city was found unsafe, inadequate and unsatisfied to mitigate seismic loads.https://www.jsoftcivil.com/article_49083_b80e5efe9fb31e24cf1bf45294ca810a.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28721220171001Development of Intelligent Systems to Predict Diamond Wire Saw Performance52694909210.22115/scce.2017.49092ENReza MikaeilDepartment of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia, Iran0000-0001-8404-3216Sina Shaffiee HaghshenasYoung Researchers and Elite Club, Rasht Branch, Islamic Azad University, Rasht, Iran0000-0003-2859-3920Yilmaz OzcelikDepartment of Mining Engineering, Hacettepe University, Ankara, TurkeySami Shaffiee HaghshenasGraduate of Civil Engineering, Department of Civil Engineering, Islamic Azad University, Astara Branch, Astara, Iran0000-0002-9301-8677Journal Article20170706Assessment of wear rate is an inseparable section of the saw ability of dimension stone, and an essential task to optimization in the diamond wire saw performance. This research aims to provide an accurate, practical and applicable model for predicting the wear rate of diamond bead based on rock properties using applications and performances of intelligent systems. In order to reach this purpose, 38 cutting test results with 38 different rocks were used from andesites, limestones and real marbles quarries located in eleven areas in Turkey. Prediction of wear rate is determined by optimization techniques like Multilayer Perceptron (MLP) and hybrid Genetic algorithm –Artificial neural network (GA-ANN) models that were utilized to build two estimation models by MATLAB software. In this study, 80% of the total samples were used randomly for the training dataset, and the remaining 20% was considered as testing data for GA-ANN model. Further, accuracy and performance capacity of models established were investigated using root mean square error (RMSE), the coefficient of determination (R<sup>2</sup>) and standard deviation (STD). Finally, a comparison was made among performances of these soft computing techniques for predicting and the results obtained indicated hybrid GA-ANN model with a coefficient of determination (R<sup>2</sup>) of training = 0.95 and testing = 0.991 can get more accurate predicting results in comparison with MLP models.https://www.jsoftcivil.com/article_49092_31e92a616e06c1d43985fa9009e1457f.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28721220171001Artificial Neural Networks for Construction Management: A Review70884958010.22115/scce.2017.49580ENPreeti SKulkarniAssociate Professor, Vishwakarma Institute of Information Technology, Pune, India0000-0003-4458-4645Shreenivas NLondheProfessor, Vishwakarma Institute of Information Technology, Pune, India0000-0001-9960-3132Makarand DeoProfessor, Indian Institute of Technology, Mumbai, IndiaJournal Article20170712Construction Management (CM) has to deal with a variety of uncertainties related to Time, Cost, Quality, and Safety, to name a few. Such uncertainties make the entire construction process highly unpredictable. It, therefore, falls under the purview of artificial neural networks (ANNs) in which the given hazy information can be effectively interpreted in order to arrive at meaningful conclusions. This paper reviews the application of ANNs in construction activities related to the prediction of costs, risk, and safety, tender bids, as well as labor and equipment productivity. The review suggests that the ANN’s had been highly beneficial in correctly interpreting inadequate input information. It was seen that most of the investigators used the feed forward back propagation type of the network; however, if a single ANN architecture was found to be insufficient, then hybrid modeling in association with other machine learning tools such as genetic programming and support vector machines were much useful. It was however clear that the authenticity of data and experience of the modeler are important in obtaining good results.https://www.jsoftcivil.com/article_49580_0129f8ca4d31af38a8558cd9148beb24.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28721220171001Predicting Budget from Transportation Research Grant Description: An Exploratory Analysis of Text Mining and Machine Learning Techniques891024960410.22115/scce.2017.49604ENAyush SinghalR&D, Contata Solutions, LLC, Minneapolis, Minnesota, USAKasthurirangan GopalakrishnanDepartment of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USASiddhartha K.KhaitanDepartment of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USAJournal Article20170812Funding agencies such as the U.S. National Science Foundation (NSF), U.S. National Institutes of Health (NIH), and the Transportation Research Board (TRB) of The National Academies make their online grant databases publicly available which document a variety of information on grants that have been funded over the past few decades. In this paper, based on a quantitative analysis of the TRB’s Research In Progress (RIP) online database, we explore the feasibility of automatically estimating the appropriate funding level, given the textual description of a transportation research project. We use statistical Text Mining (TM) and Machine Learning (ML) technologies to build this model using the 14,000 or more records of the TRB’s RIP research grants big data. Several Natural Language Processing (NLP) based text representation models such as the Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and the Doc2Vec Machine Learning (ML) approach are used to vectorize the project descriptions and generate semantic vectors. Each of these representations is then used to train supervised regression models such as Random Forest (RF) regression. Out of the three latent feature generation models, we found LDA gives the least Mean Absolute Error (MAE) using 300 feature dimensions and RF regression model. However, based on the correlation coefficients, it was found that it is not very feasible to accurately predict the funding level directly from the unstructured project abstract, given the large variations in source agencies, subject areas, and funding levels. By using separate prediction models for different types of funding agencies, funding levels were better correlated with the project abstract.https://www.jsoftcivil.com/article_49604_be480b920b0d086280457b7b0e98603d.pdf