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Velocity Field Simulation of Open-Channel Junction Using Artificial Intelligence Approaches

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2019年第1期43卷 549-560页

作者： Zaji, Amir Hossein Bonakdari, Hossein Razi Univ Dept Civil Engn Kermanshah Iran

Open-channel junctions are one of the most significant and practical structures in hydraulic engineering. Due to erosion in the contraction zone and sediment deposition in the separation zone, the ability to know the flow velocity in each point's coordinate of the junctions' main channel is a vital topic in designing them. This study aims at modeling the flow velocity in junctions using the coordinates of each point (x*, y*, and z*) and the main channel upstream to downstream discharge ratio (q*). In the modeling procedure, four powerful and well-known simulation models, namely radial basis neural network (RBNN), gene expression programming (GEP), group method of data handling (GMDH), and nonlinear regression model are used. One of the main goals of the present study is to develop accurate, simple, and explicit equations for simulation of the junctions' main channel velocity to use in practical situations. So that, all of the input variables are dimensionless to use in designing procedure of junctions with any scales. The results showed that using a simple and practical equation, GEP model with mean square error (MSE) value of 0.055 is more accurate in predicting longitudinal velocity in open-channel junctions than RBNN, GMDH, and the regression models with MSE values of 0.063, 0.075, and 0.103, respectively.

关键词： Open-channel junction Velocity field gene expression programming Radial basis neural network Group method of data handling

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Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study

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APPLIED WATER SCIENCE 2019年第7期9卷 1页

作者： Heddam, Salim Sanikhani, Hadi Kisi, Ozgur Univ 20 Aout 1955 Lab Res Biodivers Interact Ecosyst & Biotechnol Hydraul Div Agron DeptFac Sci Route El HadaikBP 26 Skikda Algeria Univ Kurdistan Agr Fac Water Sci & Engn Dept Sanandaj Iran Ilia State Univ Sch Technol GE-0162 Tbilisi Georgia

In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), were investigated in an attempt to evaluate their predictive ability of the phycocyanin pigment concentration (PC) using data from two stations operated by the United States Geological Survey (USGS). Four water quality parameters, namely temperature, pH, specific conductance and dissolved oxygen, were utilized for PC concentration estimation. The four models were evaluated using root mean square errors (RMSEs), mean absolute errors (MAEs) and correlation coefficient (R). The results showed that the ANFIS-SC provided more accurate predictions in comparison with ANFIS-GP, GEP and FFNN for both stations. For USGS 06892350 station, the R, RMSE and MAE values in the test phase for ANFIS-SC were 0.955, 0.205 mu g/L and 0.148 mu g/L, respectively. Similarly, for USGS 14211720 station, the R, RMSE and MAE values in the test phase for ANFIS-SC, respectively, were 0.950, 0.050 mu g/L and 0.031 mu g/L. Also, using several combinations of the input variables, the results showed that the ANFIS-SC having only temperature and pH as inputs provided good accuracy, with R, RMSE and MAE values in the test phase, respectively, equal to 0.917, 0.275 mu g/L and 0.200 mu g/L for USGS 06892350 station. This study proved that artificial intelligence models are good and powerful tools for predicting PC concentration using only water quality variables as predictors.

关键词： Modeling Phycocyanin concentration Feedforward neural networks gene expression programming Adaptive neuro-fuzzy inference system Grid partition Subtractive clustering

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A Proposed Model for Axial Strength Estimation of Non-compact and Slender Square CFT Columns

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2019年第1期43卷 131-147页

作者： Ahmadi, Masoud Naderpour, Hosein Kheyroddin, Ali Semnan Univ Fac Civil Engn Semnan *** Iran

The current international codes divide composite columns into three classes based on the width-to-thickness ratio of the steel tube: compact, non-compact, and slender. The present paper studies the specification of AISC 360-10 to determine the axial strength of concrete-filled steel tube (CFT) members. Three efficient approaches have been developed based on the computational intelligence technique, using a comprehensive database (experimental and numerical) of non-compact and slender square CFT columns. To achieve a wide variety in geometric and material properties of composite columns, the numerical models are simulated using OpenSEES finite element analysis package. The proposed models were created using the Levenberg-Marquardt artificial neural network, group method of data-handling approach, and gene expression programming, based on the mechanical (yield stress of tube and compressive strength of concrete) and geometrical (column length and dimensions of tube) properties of the CFT members. Comparison of the results of developed models and experimental specimens indicates superior performance and their acceptable accuracy in the determination of the axial strength of non-compact and slender CFT columns.

关键词： Slender Non-compact CFT column Axial strength gene expression programming GMDH approach Artificial neural networks

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An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement

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MEASUREMENT 2020年 152卷 107309-107309页

作者： Ashrafian, Ali Gandomi, Amir H. Rezaie-Balf, Mohammad Emadi, Mohammad Tabari Univ Babol Dept Civil Engn POB 47139-75689 Babol Sar Iran Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia Grad Univ Adv Technol Kerman Dept Civil Engn POB 76315-116 Kerman Iran

The construction and maintenance of roads pavement was a critical problem in the last years. Therefore, the use of roller-compacted concrete pavement (RCCP) in road problems is widespread. The compressive strength (f(c)) is the key characteristic of the RCCP caused to significant impact on the cost of production. In this study, an evolutionary-based algorithm named gene expression programming (GEP) is implemented to propose novel predictive formulas for the f(c) of RCCP. The f(c) is formulated based on important factor used in mixture proportion in three different combinations of dimensional form (coarse aggregate, fine aggregate, cement, pulverized fly ash, water, and binder), non-dimensional form (water to cement ratio, water to binder ratio, coarse to fine aggregate ratio and pulverized fly ash to binder ratio) and percentage form of input variables. A comprehensive and reliable database incorporating 235 experimental cases collected from several studies. Furthermore, mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (r), average absolute error (AAE), performance index (PI), and objective function (OBJ) as the internal standard statistical measures and external validation evaluated proposed GEP-based models. Uncertainty and parametric studies were carried out to verify the results. Moreover, sensitivity analysis to determine the importance of each predictor on f(c) of RCCP revealed that fine aggregate content and water to binder ratio is the most useful predictor in dimensional, non-dimensional and percentage forms, respectively. The proposed equation-based models are found to be simple, robustness and straightforward to utilize, and provide consequently new formulations for f(c) of RCCP. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Evolutionary approach Roller compacted concrete pavement gene expression programming Compressive strength Prediction

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Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)

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WATER 2020年第3期12卷 753-753页

作者： Yousefi, Peyman Courtice, Gregory Naser, Gholamreza Mohammadi, Hadi Univ British Columbia Sch Engn Kelowna BC V1V1V7 Canada Shippensburg Univ Penn Sch Engn Shippensburg PA 17257 USA

This study investigated urban water consumption complexity using chaos theory to improve forecasting performance to help optimize system management, reduce costs and improve reliability. The objectives of this study were to (1) investigate urban water distribution consumption complexity and its role in forecasting technique performance, (2) evaluate forecasting models by periodicity and lead time, and (3) propose a suitable forecasting technique based on operator applications and performance through various time scales. An urban consumption dataset obtained from the City of Kelowna (British Columbia, Canada) was used as a test case to forecast future consumption values using varying lead times under different temporal scales to identify models which may improve forecasting performance. Chaos theory techniques were employed to inform model optimization. This study attempted to address the paucity of studies on chaos theory applications in water consumption forecasting. This was accomplished by applying non-linear approximation, dynamic investigation, and phase space reconstruction for input variables, to improve the accuracy in various periodicity and lead time. To reconstruct the phase space, lag time was calculated using average mutual information for daily resolution as 17 days to reconstruct the phase space. The optimum embedding dimension and correlation exponent for the phase space were 18 and 3.5, respectively. Comparing the results, the non-linear local approximation model provided the best performance. The forecasting horizon for the models was 122 days. Moreover, phase space reconstruction improved the accuracy of the models for the different lead times. The findings of this study may improve forecasting performance and provide evidence to support further investigation of the chaotic behaviour of water consumption values over different time scales.

关键词： water consumption chaos theory local approximation Kelowna gene expression programming

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Preliminary sizing of surge vessels on pumping mains

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URBAN WATER JOURNAL 2019年第10期16卷 738-748页

作者： Sattar, Ahmed M. A. Soliman, M. El-Ansary, Amgad Cairo Univ Fac Engn Dept Irrigat & Hydraul Giza Egypt German Univ Cairo Civil Dept Cairo Egypt Cairo Univ Fac Engn Giza Egypt

Anew and simple model for sizing surge vessels on pumping mains has been developed. The Monte Carlo Simulation (MCS) is utilized to generate 2000 realizations for various cases of up- and down-surges. Approximate models are developed for the prediction of the initial and the expanded vessel's gas volume. The developed models outperformed the existing design charts;yielding better scores in various statistical indicators, e.g. a high coefficient of determination of 0.94 and four orders of magnitude less error. The vessel's initial gas volume is found to be affected mostly by the pipe diameter and static head, while the expanded gas volume depends on the initial gas volume and the static head. The error analysis revealed that the predicted models had a low error band of 0.36 compared to 0.78 for previous design charts. These developed approximate models can serve as a basis for preliminary surge protection size selection during feasibility study phases.

关键词： Surge vessel water hammer hydraulic transients gene expression programming method of characteristics Monte Carlo Simulation

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Rockburst assessment in deep geotechnical conditions using true-triaxial tests and data-driven approaches

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INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 2020年 128卷 104279-104279页

作者： Faradonbeh, Roohollah Shirani Taheri, Abbas Ribeiro e Sousa, Luis Karakus, Murat Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia State Key Lab Geomech & Deep Underground Engn Beijing Peoples R China Univ Porto Porto Portugal

Deep underground excavations in mining and civil engineering are subjected to high in-situ stresses which can cause rockburst. Rockburst is an instantaneous release of a large amount of strain energy stored in rockmass that can lead to injuries, deaths, and damage to infrastructures. Many studies have been done regarding rockburst, however, there is no practical model to predict the stress level that rockburst occurs (i.e. maximum rockburst stress) and its related risk (i.e. rockburst risk index) based on real rockburst tests, and the main rock mechanical properties. In this study, a comprehensive database of true-triaxial unloading tests on rocks having a wide range of properties was compiled. The agglomerative hierarchical clustering (AHC) analysis was carried out on the original database to evaluate the presence of natural groups and outliers. Then, the stepwise selection and elimination (SSE) procedure were employed for dimension reduction of the problem and identifying the most influential attributes on rockburst parameters. Afterward, two robust non-linear algorithms, including gene expression programming (GEP) and classification and regression tree (CART) were used to develop the predictive models for rockburst maximum stress and its risk index. The validation verification of the proposed models using several indices proved the high prediction performance of the developed non-linear models. Finally, a parametric analysis was carried out to evaluate the influence of each input parameter on the corresponding output. The proposed models in this study are practical and do not require any presupposition about rockburst mechanism, which makes them be used easily in practice by engineers at the design and progress stages of the underground projects.

关键词： Rockburst maximum stress Rockburst risk index True-triaxial test gene expression programming Classification and regression tree

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Computational predictions for estimating the maximum deflection of reinforced concrete panels subjected to the blast load

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INTERNATIONAL JOURNAL OF IMPACT ENGINEERING 2020年第0期139卷 103527-000页

作者： Shishegaran, Aydin Khalili, Mohammad Reza Karami, Behnam Rabczuk, Timon Shishegaran, Arshia Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Sharif Univ Technol Tehran Sch Civil Engn Tehran Iran Int Inst Earthquake Engn & Seismol Tehran Iran Ton Duc Thang Univ Div Computat Mech Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Islamic Azad Univ Sch Civil Engn Tehran Iran

We investigate the resistance of reinforced concrete panels (RCPs) due to explosive loading using nonlinear finite element analysis and surrogate models. Therefore, gene expression programming model (GEP), multiple linear regression (MLR), multiple Ln equation regression (MLnER), and their combination are used to predict the maximum deflection of RCPs. The maximum positive and negative errors, mean of absolute percentage error (MAPE), and statistical parameters such as the coefficient of determination, root mean square error (RMSE). Normalized square error (NMSE), and fractional bias are utilized to evaluate and compare the performance of the models. We also present a novel statistical table to demonstrate the distribution of percentage errors indicating that MLnER is the best model for predicting the maximum deflection of RCPs under blast loading. We also carry out a detailed parameter study. The independent variables include the weight of charge, standoff distance, panel thickness, panel dimensions, reinforcement ratio, the compressive strength of concrete and yield strength of the reinforcement. We find that the key parameters are the panel thickness and compressive strength with respect to the explosive strength of RCPs, and the explosive weight and distance from the explosive have the most impact on the RCP failure.

关键词： Reinforced Concrete Panels (RCP) Explosive load Finite Element Method (FEM) Parametric study Regression models gene expression programming Ensemble model

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Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method

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JOURNAL OF BUILDING ENGINEERING 2020年 31卷

作者： Shahmansouri, Amir Ali Bengar, Habib Akbarzadeh Ghanbari, Saeed Univ Mazandaran Dept Civil Engn Babol Sar Iran

Geopolymer concrete (GPC) could be used as an environmental-friendly alternative solution for concrete production due to the detrimental impacts of cement production on the environment. Since obtaining an optimal mix design, and subsequently, predicting the compressive strength of mortar using experimental means is costly and time-consuming, employing soft-computing techniques could facilitate and accelerate this approach. In this research, gene expression programming (GEP) was used to develop numerical models for predicting the compressive strength of GPC based on ground granulated blast-furnace slag (GGBS). Through an experimental program, an extensive database of the compressive strength of GGBS-based GPC consisting of 351 specimens obtained from 117 different mixtures was generated. The five most effective parameters, including specimen age, sodium hydroxide (NaOH) solution concentration, natural zeolite (NZ), silica fume (SF), and GGBS content, were considered as the modeling input parameters. Using GEP algorithm, simplified and practical mathematical equations were proposed to predict the compressive strength of GGBS-based GPC mortar. Performance, high accuracy, and predictability of the proposed equations were validated by the conducted sensitivity and parametric analyses. The obtained results could promote the re-use of GGBS for GPC development, which in turn will lead to environmental and economic advantages.

关键词： Geopolymer concrete Ground granulated blast-furnace slag Compressive strength Strength prediction gene expression programming

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Application of Soft Computing Techniques for Particle Froude Number Estimation in Sewer Pipes

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JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2020年第2期11卷

作者： Mehr, Ali Danandeh Safari, Mir Jafar Sadegh Antalya Bilim Univ Dept Civil Engn TR-07190 Antalya Turkey Yasar Univ Dept Civil Engn TR-35100 Izmir Turkey

Sedimentation in sewer networks is a major problem in urban hydrology. In comparison to the well-known classic sediment transport models, this study investigates the capabilities of soft computing methods, including multigene genetic programming (MGGP), gene expression programming, and multilayer perceptron to derive accurate sewer design models. A wide range of experimental data sets comprising fluid, flow, sediment, and pipe features was used to develop new models under the nondeposition with a deposited bed self-cleansing condition. The results showed better performances of the new models compared to the conventional ones in terms of statistical performance indices. The proposed MGGP model was found superior to its counterparts. It is an explicit model motivated to be used for self-cleansing sewer pipes design in practice.

关键词： Bed load Sediment transport Sewer network Multigene genetic programming gene expression programming

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