Pouyan PressJournal of Soft Computing in Civil Engineering2588-28723320190701Adaptive Neuro-Fuzzy and Simple Adaptive Control Methods for Attenuating the Seismic Responses of Coupled Buildings with Semi-Active Devices: Comparative Study1219579610.22115/scce.2019.199731.1128ENOmar A.S.Al-FahdawiPh.D. Candidate, Texas A&M University, Zachry Department of Civil and Environmental Engineering, College Station, TX 77843, United States0000-0001-9623-9371Luciana R.BarrosoAssociate Professor, Texas A&M University, Zachry Department of Civil and Environmental Engineering, College Station, TX 77843, United StatesRachel W.SoaresPh.D. Candidate, Texas A&M University, Zachry Department of Civil and Environmental Engineering, College Station, TX 77843, United StatesJournal Article20190915This paper describes two adaptive control methods for mitigating the seismic responses of two connected buildings with MR dampers at different levels. First method developed in this study is the adaptive neuro-fuzzy controller which consists of a fuzzy logic controller provided with learning algorithm based on adaptive neural networks. The learning algorithm is implemented to modify the parameters of the fuzzy logic controller such that its outputs track the behavior of predetermined training data. Second method is the simple adaptive controller which falls into the category of model-following adaptive strategies. In this method, a plant is commanded to follow a well-designed reference-model with desirable trajectories through a closed loop action. The coupled system consists of two adjacent buildings having different heights in order to separate the model shapes of the individual buildings. Different types of feedbacks such as displacement, velocity, and acceleration are employed to identify their impacts on the performance of the developed adaptive controllers. Numerical analyses are carried out for the complex system assuming no change in the nominal design parameters and then for the system where a change in these parameters is introduced. The results reveal that using the adaptive controllers developed in this study to regulate the MR dampers connecting the two adjacent buildings can successfully alleviate the seismic responses under various types and intensities of earthquakes.https://www.jsoftcivil.com/article_95796_b06da9468333da2eae7984ce9bbba443.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28723320190701Forecasting of Wind-Wave Height by Using Adaptive Neuro-Fuzzy Inference System and Decision Tree22369594910.22115/scce.2019.199291.1125ENLyaghat BozorgzadehGraduate Master of Science of Coastal, Port and Marine Science Engineering, Khorramshahr University of Marine Science and Technology, Khorramshahr, IranMorteza BakhtiariAssistant Professor, Khorramshahr University of Marine Science and Technology, Khorramshahr, IranNima Shani Karam ZadehAssistant Professor, Khorramshahr University of Marine Science and Technology, Khorramshahr, IranMohammad EsmaeeldoustAssistant Professor, Khorramshahr University of Marine Science and Technology, Khorramshahr, IranJournal Article20190826Wind-induced waves are considered to be the most important waves in the sea due to their high energy and frequency. Among the characteristics of the waves, height is one of the most important parameters that are used in most equations related to marine engineering designs. Since the application of soft computing methods in marine engineering has been developed in recent years, in present research, an adaptive neuro-<em>fuzzy inference system </em>and a decision tree have been used to predict the wind-induced wave height in Bushehr port. In order to identify the effective parameters, implementing different models from different inputs. By considering the accuracy of the models, the effective parameters in wave height were identified using statistical measures correlation coefficient (r), Mean Square Error (MSE). The final results of this study showed that in the prediction of wind-induced wave height, compared to the decision tree, the accuracy of the model of the neural-fuzzy system for 3, 6 and 9 hours was higher. Also, the results showed that the use of wind shear velocity instead of wind speed at 10 meters above the water level had a higher accuracy in forecasting of the significant wave height. The results also indicated that among the presented models, the combined model of the significant wave height, shear velocity, and the difference between the direction and wind speed as well as the length of the fetch has the highest accuracy.https://www.jsoftcivil.com/article_95949_3fd98439b665650122c96e68d1270bc1.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28723320190701Estimating Channel Cross Section Runoff Overflow Using Fuzzy Rule Based System: A Hydrologic Analysis of Mt. Isarog Watershed37469596010.22115/scce.2019.201080.1127ENRaymundo VargasRomeroProfessor, College of Engineering, Partido State University, Goa, Camarines Sur, Philippines0000-0002-7142-5749Marijane AtoleIglesiaAssistant Professor, College of Engineering, Partido State University, Goa, Camarines Sur, PhilippinesAlicia SalamatPempenaAssistant Professor, College of Engineering, Partido State University, Goa, Camarines Sur, Philippines0000-0001-5450-0746Nelson VargasRomeroAssociate Professor, College of Engineering, Partido State University, Goa, Camarines Sur, PhilippinesFe B.RomeroAssociate Professor, College of Education, Partido State University, Goa, Camarines Sur, PhilippinesJournal Article20190909This study estimated discharges of a watershed based from twenty four hour recorded precipitation of year 2018 using modified soil conservation system (SCS-CN) method. This established fuzzy rule based system which ultimately was used to estimate the sufficiency of river cross sectional area to accommodate water discharges on a river channel. The highest river flow of the month was described. Rain gauge was used in collecting daily rainfall data. Pattern recognition method was used in computing watershed area through satellite images. The method was also used in identifying areas affected by overflow. The process was centered on the cross sectional area of the river which eventually was used in computing the amount of river discharges. The highest precipitation event of the month of December has found that the river cross sectional area is insufficient to accommodate the accumulated rain water. Traces of overflow could be seen in satellite images.https://www.jsoftcivil.com/article_95960_1dc9d9d6cb73923a9558ab9721b2539e.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28723320190701Optimal Operation of Dam Reservoir Using Gray Wolf Optimizer Algorithm (Case Study: Urmia Shaharchay Dam in Iran)476110085410.22115/scce.2020.189429.1112ENYahya ChoopanPh.D. Candidate of Irrigation and Drainage, Department of Water Engineering, Gorgan University of Agriculture Sciences and Natural Resources, Gorgan, IranSomayeh EmamiPh.D. Candidate of Hydraulic Structures, Department of Water Engineering, University of Tabriz, Tabriz, Iran0000-0001-8034-4652Journal Article20190610Reservoir storage prediction is so crucial for water resources planning and managing water resources, drought risk management and flood predicting throughout the world. In this study, Gray Wolf Optimizer algorithm (GWO) was applied to predict Shaharchay dam reservoir storage of located in the Urmia Lake basin, northwest of Iran. The results of the GWO algorithm have been compared with the continuous genetic algorithm (CGA). The predicted values from the GWO algorithm matched the measured values very well. According to the results, the error is not significant (2.11%) in the implementation of the GWO and the correlation coefficient between the predicted and measured values is 0.92. In addition, the statistical criteria of RMSE, MAE and NSE for GWO algorithm were estimated to be 0.03, 0.41 and 0.74, respectively, indicated a satisfactory performance. Excessive value of correlation coefficient expresses that the GWO algorithm pretty suit the variables and may finally be used for predicting of reservoir storage for operational overall performance. Comparison of results showed that the GWO algorithm with average best objective function value of 121, 112 and 83.10 with a number of further evaluations of the objective function to achieve higher capacity is the optimum answer.https://www.jsoftcivil.com/article_100854_fc914d9b83b1d91da8579836157f9cbc.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28723320190701Structural Optimization of Concrete Volume for Machine Foundation Using Genetic Algorithms62819672710.22115/scce.2019.203066.1129ENMatheus AbreuLopesD.Sc. Student, Mechanical Engineering Postgraduate Program (PPGEM), State University of Rio de Janeiro (UERJ), Rio de Janeiro/RJ, BrazilFrancisco Jose Da CunhaPires SoeiroProfessor, Mechanical Engineering Postgraduate Program (PPGEM), State University of Rio de Janeiro (UERJ), Rio de Janeiro/RJ, BrazilJose Guilherme Santos Da SilvaProfessor, Civil Engineering Postgraduate Program (PGECIV), State University of Rio de Janeiro (UERJ), Rio de Janeiro/RJ, Brazil0000-0002-2407-2127Journal Article20190927This research work aims to optimize a concrete foundation designed to support a high-capacity motor-driven compressor. The structure has plane dimensions of approximately 15 m × 11 m and a height of 1.5 m. The concrete block is to be supported by 20 concrete piles approximately 8.5 m in length and 0.5 m in diameter. The investigated structural system is subjected to deterministic dynamic loadings due to the nature of the equipment supported by the concrete foundation. The main objective of the optimization is to reduce the structural volume through the analysis of its dynamic response, in order to minimize the cost of the concrete volume. In this research work, Genetic Algorithms (GAs) are used through an appropriate interface between ANSYS and MATLAB software. The results of this study show that through the GAs it is possible to achieve a considerable volume reduction with respect to the original volume of concrete used in the design of the foundations structural system.https://www.jsoftcivil.com/article_96727_f7af9b39bc5af9b08dea78758c461104.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28723320190701Mesoscopic Generation of Random Concrete Structure Using Equivalent Space Method829410184810.22115/scce.2020.171191.1096ENBijan SayyafzadehPh.D. Student, Department of Civil Engineering, Qom University, Qom, Iran0000-0002-1647-9541Ali OmidiM.Sc. Graduated, Department of Civil Engineering, Shahid Chamran University of Ahvaz, Ahvaz, IranIraj RasoolanAssistant Professor, Department of Civil Engineering, Shahid Chamran University of Ahvaz, Ahvaz, IranJournal Article20190206Concrete is a composite material with a wide variety of inhomogeneity. The mechanical behavior of concrete depends on the properties of its components. Mesoscopic model which treats concrete as a heterogeneous material consisting of coarse aggregates, mortar matrix with fine aggregates dissolved in it and Interfacial Transition Zone (ITZ) provides an effective approach to study how the properties of concrete components can affect its mechanical behavior. For such a study it is first necessary to generate a random concrete structure that resembles real concrete specimens. In this paper, an efficient simulation method for generating random concrete structure at mesolevel based on Monte Carlo random sampling principle is outlined and compared with two other most frequently used methods. A new method, the ‘equivalent space method’, appears to be more convenient for both low and high volume fraction specimens. In this method with each random selection of a value as the position of an aggregate particle with a definite size, more options for its position will be reached and examined. This leads to more realistic concrete models with less random numbers.https://www.jsoftcivil.com/article_101848_3af622f3456be0184d5dabd6eaf76372.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28723320190701Optimization-Based Design of 3D Reinforced Concrete Structures9510610085510.22115/scce.2020.211509.1145ENHamid DehnavipourFaculty of Civil Engineering, Semnan University, Semnan, Iran0000-0003-3605-613XMehrnaz MehrabaniM.Sc., Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, IranAhmadreza FakhriyatM.Sc., Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, IranAnna Jakubczyk-GałczyńskaAssistant Professor, Department of Construction Management and Earthquake Engineering, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gdansk, Poland0000-0003-4616-0010Journal Article20191212In the design of reinforced concrete (RC) structures, finding the optimal section of members and the optimal rebar, which is capable of observing building code’s requirements, is always the primary concern to engineers. Since an optimal design needs a trial-and-error approach, which designs are almost assumed without this approach, that is unlikely to lead to the best solution. Therefore, in this article, the aim is achieving an optimal structural design that can satisfy the building code’s requirements, such as constraints on flexural strength, shear strength, drift, and constraint of construction at the same time. The work is presented in this paper intends to accelerate the process with an optimization system. To do so, a six-story RC structure analyzed by the linear static method and results of the optimization process, done by the Particle Swarm optimization algorithm (PSO), has shown that the weight of the structure optimized and observed limitations.https://www.jsoftcivil.com/article_100855_63a6a25ca9028078b1dba562b1fdbac9.pdf