Pouyan PressJournal of Soft Computing in Civil Engineering2588-28725320210701Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load11413342810.22115/scce.2021.281972.1300ENHojjat EmamiAssistant Professor of Computer Engineering, Faculty of Engineering, University of Bonab, Bonab, IranSomayeh EmamiPh.D. Candidate of Hydraulic Structures, Department of Water Engineering, University of Tabriz, Tabriz, Iran0000-0001-8034-4652Journal Article20210419In Iran, no detailed information on the amount of erosion, sediment transport, and sedimentation of rivers, and in many cases, there are many differences between measurements. Since the flow regime and consequently the sediment regime in the drainage basins are not constant, estimation of sediment discharge can help estimate the sediment accumulated behind the water structures, especially the dams, and determining the dead volume of reservoirs in the coming months, and by adopting timely arrangements, the management of discharge will be facilitated to a certain extent during sedimentation. In this study, a hybrid method of the Whale optimization algorithm and the neuro-fuzzy inference system was used to estimate the suspended sediment load (<em>SLL</em>) of the Zarinehrood river. The performance of the proposed methods was evaluated by two statistics, including determination coefficient (R<sup>2</sup>) and normal root mean square error (NRMSE). <em>SSL</em> of the Zarinehrood river during 10 years with flow discharge was used as inputs. The results showed the high accuracy of the WOA-ANFIS with values R<sup>2</sup>=0.962 and NRMSE=0.051. In general, a comparison of the results obtained from the hybrid method used in this study showed the high ability and accuracy of the WOA-ANFIS method in estimating the <em>SLL</em> of the Zarinhrood river.https://www.jsoftcivil.com/article_133428_6eced291a0f67838f56a6b74fa85ed8a.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28725320210701Experimental Investigation and Modeling of Aeration Efficiency at Labyrinth Weirs153113483310.22115/scce.2021.284637.1311ENAradhana SinghCivil Engineering Department, National Institute of Technology, Kurukshetra, IndiaBalraj SinghCivil Engineering Department, Panipat Institute of Engineering and Technology, Samalkha, India0000-0002-0381-4363Parveen SihagCivil Engineering Department, Shoolini University, Solan, India0000-0002-7761-0603Journal Article20210504For maintaining healthy streams and rivers, a high concentration of oxygen is desired and hydraulic structures act as natural aerators where oxygen transfer occurs by creating turbulence in the water. Aeration studies of conventional weirs are carried out widely in the past but at the same time, labyrinth weirs, where the weir crest is cranked thereby enhancing their crest length, have got a little notice. The test records were obtained through 180 laboratory observations on nine physical models to estimate aeration efficiency (E<sub>20</sub>) at labyrinth weirs (LWs). The E<sub>20</sub> increases with the number of key as well as drop height and it is found to be highest for rectangular shape in comparison of the triangular and trapezoidal LWs, however, E<sub>20</sub> decreases with the increase of discharge. Further, this work unravels the novel idea and potential of the M5P model tree (M5P), support vector regression machine (SVM), and Random Forest (RF) methods for estimation of aeration efficiency (E<sub>20</sub>) at LWs. The results depicted that the RF model performs best in determining the E20 at LWs. The results of sensitivity analysis further illustrated that drop height is the parameter that affects the prediction of E<sub>20</sub> at the LWs most.https://www.jsoftcivil.com/article_134833_c59fd08786dfd496d06600f278ed047f.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28725320210701Performance Evaluation of Machine Learning Algorithms for Seismic Retrofit Cost Estimation Using Structural Parameters325713492910.22115/scce.2021.284630.1312ENNaser Safaeian HamzehkolaeiAssistant Professor, Department of Civil Engineering, Bozorgmehr University of Qaenat, Qaen, Iran0000-0003-2112-2341Meysam AlizamirPh.D., Department of Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, IranJournal Article20210505Estimation of the seismic retrofit cost (SRC) is a complicated task in construction projects. In this study, the performance of four machine learning algorithms (MLAs), including Random Forest (RF), Extreme Learning Machine (ELM), Classification and Regression Tree (CART), and Multivariate Adaptive Regression Spline (MARS), was examined in estimating SRC values. The total floor area (TFA), number of stories (NS), seismic weight (SW), seismicity (S), soil type (ST), plan configuration (PC), and structural type (STT) were considered as structural input variables. To achieve the best performance of applied MLAs, twenty-two scenarios based on different combinations of input variables were considered. The correlation coefficient (r), Root Mean Squared Error (RMSE), Adjusted R-squared, and Nash-Sutcliffe efficiency (NSE) metrics together with the Taylor diagram were used to compare the accuracy of applied models. A sensitivity analysis using the RReliefF algorithm showed that TFA, SW, and PC are the most influential parameters, whereas the ST and STT have negative influences on SRC values. Comparison analysis results indicated that the ELM model with r of 0.896, RMSE of 0.081, and NSE of 0.758 had the best performance among other employed MLAs. Also, the RF regression achieved the second rank. In conclusion, the ELM model with single-layer feedforward neural network was superior to other data-driven models; therefore, it can be applied as an efficient tool for estimating SRC values using structural input parameters.https://www.jsoftcivil.com/article_134929_006f74c73227399a0d757559c91f64c9.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28725320210701A Comparative Review of Image Processing Based Crack Detection Techniques on Civil Engineering Structures587413676610.22115/scce.2021.287729.1325ENMd. Rahat ShahriarZawadDepartment of Information and Communication Technology, Bangladesh University of Professionals (BUP), Dhaka, Bangladesh0000-0001-9047-7272Md. Fahad ShahriarZawadDepartment of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chattogram, Bangladesh0000-0002-0277-3694Md. Asifur RahmanDepartment of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chattogram, BangladeshSudipto NathPriyomDepartment of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chattogram, BangladeshJournal Article20210524Crack detection and repair of the cracks in engineering structures is essential to ensure serviceability and durability. Traditionally, cracks are detected by the examiner's visual inspection; as a result, crack detection and estimation of characteristics are greatly dependent on the examiner's personal judgment, which has aided in the repair of various structures and evaluation of the crack phenomenon in previous decades. Due to industrial advancement, the number of engineering structures has increased, but compared to that, expertise in the crack detection field did not raise that level. So, a less time-consuming and more accurate approach is needed. The image processing technique works simultaneously to detect the cracks with their attributes. In this context, the development of the algorithm and the implementation procedure is also simple. But some defects such as identifying noises as cracks and weakness in identifying micro-cracks have become significant challenges for this technique. Unable to locate transverse cracks in concrete structures is also a vital issue. So, to develop an accurate method, an extensive survey on the current articles is needed. In this paper, a critical analysis has been done on crack detection through the image processing phenomenon and a detailed literature review to understand the prospects of this method. From the literature review, it was observed that a general structure of CNN-based algorithm with camera images for crack detection could be an efficient approach with higher accuracy.https://www.jsoftcivil.com/article_136766_68c5194a2dae19e0da1623236bf6d70d.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28725320210701Simulation of Pan Evaporation Rate by ANN Artificial Intelligence Model in Damghan Region758713676710.22115/scce.2021.286933.1321ENSina ShahiFaculty of Civil Engineering, Semnan University, Semnan, IranSayed Farhad MousaviProfessor, Faculty of Civil Engineering, Semnan University, Semnan, IranKhosrow HosseiniFaculty of Civil Engineering, Semnan University, Semnan, IranJournal Article20210518Regarding different aspects of management of drainage basins and droughts, prediction of evaporation is very important. Evaporation is an essential part of the water cycle and plays an important role in the evaluation of climatic characteristics of any region. The purpose of this research is to predict daily pan evaporation rate of Damghan city using an artificial neural network model. The data applied in this research are daily minimum and maximum temperatures, average relative humidity, wind speed, sunshine hours, and evaporation during the statistical time period of 16 years (2002-2018). Also, the artificial neural network was used as a non-linear method to simulate evaporation. Since the units of the inputs and outputs of the prediction model were different, all the data were normalized. In the ANN model, seven different scenarios were considered. About 70 and 30 percentage of the data were used for training and testing, respectively. The model was analyzed by appropriate statistics such as coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), and mean absolute error (MAE). Results showed that the seventh scenario including minimum and maximum temperatures, average relative humidity, wind speed, sunshine hours, and pressure proved to be the superior scenario among others. The values of R<sup>2</sup>, RMSE, and MAE for the superior scenario were 0.8030, 2.75 mm/day, and 1.88 mm/day, respectively.https://www.jsoftcivil.com/article_136767_e22203b5c6ba0df00dac7a27e3d05a5b.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28725320210701Deep Neural Network-Based Storey Drift Modelling of Precast Concrete Structures Using RStudio8810013940510.22115/scce.2021.289034.1329ENNitin DahiyaPh.D. Student, Faculty of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, IndiaBabita SainiAssociate Professor, Faculty of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, IndiaH. D.ChalakAssistant Professor, Faculty of Civil Engineering, National Institute of Technology Kurukshetra, Haryana, IndiaJournal Article20210603In this research, storey drift has been determined using Deep neural networks (DNN Keras). DNN Keras has various hyper tuning parameters (hidden layer, drop out layer, epochs, batch size and activation function) that make it capable to model complex problems. Building height, number of bays, number of storeys, time period, storey displacement, and storey acceleration were the input parameters while storey drift was the output parameter. The dataset consists of 288 models, out of 197 were used as training data and the remaining 91 were used as test data. 0.9598 correlation coefficient was observed for DNN Keras as compared to 0.8905 by resilient back-propagation neural networks (BPNN), indicating that DNN Keras has about 8 per cent improved efficiency in predicting storey drift. Wilcoxon signed-rank test (non-parametric test) was used to compare and validate the performance of DNN Keras and resilient BPNN algorithms. The positive results of this study point to the need for further research into the use of DNN Keras in structural and civil engineering.https://www.jsoftcivil.com/article_139405_95df3188de9b0fd1727ae74f13214977.pdfPouyan PressJournal of Soft Computing in Civil Engineering2588-28725320210701Damage Localization of RC Beams via Wavelet Analysis of Noise Contaminated Modal Curvatures10113313940810.22115/scce.2021.292279.1340ENHashem JahangirAssistant Professor, Department of Civil Engineering, University of Birjand, Birjand, Iran0000-0003-3099-4045Hamed HasaniM.Sc. Student, Department of Civil Engineering, University of Birjand, Birjand, Iran0000-0002-2241-551XMohammad Reza EsfahaniProfessor, Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran0000-0002-2655-2466Journal Article20210626In this paper, the location of single, double, and triple damage scenarios in reinforced concrete (RC) beams are assessed using the Wavelet transform coefficient. To achieve this goal, the numerical models of RC concrete beams were conducted based on the experimental specimens. The mode shapes and corresponding modal curvatures of the RC beam models in damaged and undamaged status were considered as input signals in Wavelet transform. By considering the Wavelet coefficient as damage index, Daubechies, Biorthogonal, and Reverse Biorthogonal Wavelet families were compared to select the most proper one to identify damage locations. Moreover, various sampling distances and their influence on the damage index were studied. In order to simulate the practical situations, two kinds of noises were added to modal data and then denoised by Wavelet analysis to check the proposed damage index in noisy conditions. The results revealed that among the wavelet families, rbio2.4 and rbio2.2 outperform others in detecting damage locations using mode shapes and modal curvatures, respectively. As expected, the sensitivity of modal curvatures to different damage scenarios is more the mode shapes. By increasing sampling distances from 25 mm to 100 mm, the accuracy of the proposed damage index reduces. In order to eliminate boundary effects, it is necessary to use windowing techniques. Applying Wavelet denoising methods on noise-contaminated modal curvatures leads to proper damage localization in both types of noises.https://www.jsoftcivil.com/article_139408_792993b98990ba630eeac3152df731e4.pdf