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Sediment transport modeling in rigid boundary open channels using generalize structure of group method of data handling

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JOURNAL OF HYDROLOGY 2019年 577卷 123951-000页

作者： Safari, Mir Jafar Sadegh Ebtehaj, Isa Bonakdari, Hossein Es-haghi, Mohammad Sadegh Yasar Univ Dept Civil Engn Izmir Turkey Razi Univ Dept Civil Engn Kermanshah Iran Khajeh Nasir Toosi Univ Technol Sch Civil Engn Tehran Iran

Sediment transport in open channels has complicated nature and finding the analytical models applicable for channel design in practice is a quite difficult task. To this end, behind theoretical consideration of the open channel sediment transport through incorporating of four fundamental characteristics of fluid, flow, sediment and channel, recently, machine learning techniques are used for modeling of sediment transport in open channels. However, most of the studies in the literature used limited number of data for model development neglecting some effective parameters involved which may affect their performances. Moreover, most of this studies had not provided a comprehensive explicit equation for future use. Accordingly, this study applied four machine learning techniques of gene expression programming (GEP), Extreme Learning Machine (ELM), generalized Structure Group Method of Data Handling (GS-GMDH) and Fuzzy c-means based Adaptive Neuro-Fuzzy Inference System (FCM-ANFIS) to model sediment transport in open channels. Four existing data sets in the literature with wide ranges of pipe size, sediment size, sediment volumetric concentration, channel bed slope and flow depth are used for the model development. The recommended models are compared with their corresponding conventional regression models taken from the literature in terms of different statistical performance indices. Results indicate superiority of the machine leaning techniques to the conventional multiple non-linear regression models. Although, developed GEP, ELM, GS-GMDH and FCM-ANFIS models have almost same performances, GS-GMDH gives slightly better performance which can be linked to the generalized structure of this approach. A MATLAB code is provided to calculate the sediment transport in open channel for practical engineering.

关键词： Extreme learning machine Fuzzy c-means based adaptive neuro-fuzzy inference system gene expression programming generalized structure of group method of data handling Rigid boundary channel Sediment transport

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Predictive modelling of sustainable lightweight foamed concrete using machine learning novel approach

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JOURNAL OF BUILDING ENGINEERING 2022年 56卷

作者： Ullah, Haji Sami Khushnood, Rao Arsalan Ahmad, Junaid Farooq, Furqan Natl Univ Sci & Technol NUST NUST Inst Civil Engn NICE Sch Civil & Environm Engn SCEE Sect H-12 Islamabad 44000 Pakistan Minist Def MoD Mil Engineer Serv MES Rawalpindi 43600 Pakistan

Foamed concrete is a versatile material that can be used in different construction applications and with proper mix designing, it can also be used as a structural member. The production of sustainable lightweight foamed concrete (LWFC) requires a proper mix design relation to achieve the desired physical and mechanical properties. There are a few standards such as ACI 211.2 that give mix design procedures, but the provided methods cannot be applied for all forms of lightweight concrete, particularly for LWFC. This study aimed to develop a relationship between the mix design parameters, density, and compressive strength using gene expression programming. For the development of models, an extensive database of 191 data points was collected from the published literature that comprised of cement content, sand content, water to cement ratio, foam volume as input parameters, and the dry density and 28 days compressive strength as an output parameter. The developed models were evaluated by using a regression tool i.e. coefficient of determination (R-2) along with the root mean square error, mean absolute error, root square error, objective function and performance index. A strong correlation of R-2 0.95, was obtained for both the density and strength models along with the least statistical errors. From the results, it has been observed that 95% of the predicted results have error values less than 2% for the density model and 91% of the predicted results have error values less than 5 MPa for the strength model. The validity of the models was further verified by conducting an experimental investigation where a satisfactory relation of R-2 0.79 and 0.94 was observed for density and compressive strength models, respectively. In addition, the sensitivity and parametric analysis were also performed to analyze the influence of individual input parameters in relation to the output. The developed GEP models are expressed in the form of empirical relations that can be used for the p

关键词： Foamed concrete Sustainable concrete Compressive strength gene expression programming Machine learning Artificial intelligence Sensitivity and parametric analysis genetic algorithms

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Prediction of discharge flow rate beneath sheet piles using scaled boundary finite element modeling database

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SCIENTIA IRANICA 2021年第2期28卷 645-655页

作者： Johari, A. Heydari, A. Talebi, A. Shiraz Univ Technol Dept Civil & Environm Engn POB 71557-13876 Shiraz Iran

Sheet piles are of general water-retaining structures. The rate of discharge flow beneath sheet plies is an important parameter in the design of these structures. In this study, gene expression programming (GEP) as an artificial intelligence method is used for developing a model to predict the discharge flow rate. The input parameters include the sheet pile height, upstream head, and hydraulic conductivity anisotropy ratio. In order to achieve better performance, the flow rate is normalized and selected as an output of the model. A database including 1000 cases created from the Scaled Boundary Finite Element Method (SBFEM) for the seepage beneath sheet plies is employed to develop the model. The GEP-based model predictions are shown in reasonable agreement with the simulated data, which indicates the efficiency of the developed model. The results of the sensitivity analysis indicate that the upstream head is the most influential parameter in the discharge flow rate beneath the sheet piles. Furthermore, the outputs of the parametric analysis show the reasonable performance of the model in the prediction of normalized discharge flow rate. (C) 2021 Sharif University of Technology. All rights reserved.

关键词： Seepage Scaled boundary finite-element method Flow rate discharge Sheet pile gene expression programming

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Estimation model of leaf nitrogen content based on GEP and leaf spectral reflectance

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 98卷 107648-107648页

作者： Yang, Lechan Deng, Song Ma, Shouming Xiao, Fangxiong Jailing Inst Technol Dept Soft Engn Nanjing 211169 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China

High-precision estimation of leaf nitrogen content is essential for monitoring and evaluating the quality of crop products. So, it is necessary to find out the bands that affect the estimation of leaf nitrogen content from the more original hyperspectral bands, which is very important to improve the accuracy of leaf nitrogen estimation. However, the calculation complexity based on multiple regression is relatively high and the multiple regression model needs to rely on prior knowledge. This paper proposes an estimation model of leaf nitrogen content based on gene expression programming (GEP) and leaf spectral reflectance. The experimental results show that compared with the other regression algorithms, the first derivative local band model obtained based on the GEP has better estimation accuracy, and the selected local band is less affected by moisture.

关键词： gene expression programming Leaf nitrogen content Regression Hyperspectral band

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Evolutionary artificial intelligence approach for performance prediction of bio-composites

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CONSTRUCTION AND BUILDING MATERIALS 2021年 290卷 123254-123254页

作者： Ahmad, Muhammad Riaz Chen, Bing Dai, Jian-Guo Kazmi, Syed Minhaj Saleem Munir, Muhammad Junaid Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Civil Engn Shanghai 200240 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China RMIT Univ Sch Engn 376-392 Swanston St Melbourne Vic 3001 Australia

Giving the high amount of carbon and energy emission from the use of traditional building materials, the use of bio-composites made from industrial crops especially hemp has caught attention from researchers in recent years. These bio-composites not only enhance the thermal performance of buildings but also promote sustainable development due to their eco-friendly nature. Due to their highly heterogeneous nature, however, most of the existing studies on the bio-composites have only focused on experimental investigations, while mathematical modeling of physical, thermal and mechanical properties of biocomposite remains a challenge for the researchers. In this paper, an artificial intelligence (AI) based gene expression programming (GEP) technique is used to develop the mathematical models for predicting the dry density, compressive strength and thermal conductivity of hemp-based bio-composites. A large amount of database was established based on past studies and the most influential parameters were identified by several trial analyses. The proposed mathematical models showed a high correlation with the experimental results. All the models passed the statistical and performance index checks showing strong predictability, generalization capability and high accuracy of GEP-AI models. Comparison of results with the regression analysis techniques further proved the superiority of GEP-AI models over the traditional methods. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： gene expression programming Bio-composite Thermal conductivity Compressive strength Mathematical modeling

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Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 132卷

作者： Abarkan, Ikram Rabi, Musab Ferreira, Felipe Piana Vendramell Shamass, Rabee Limbachiya, Vireen Jweihan, Yazeed S. Santos, Luis Fernando Pinho Abdelmalek Essaadi Univ Fac Sci Dept Phys Tetouan 93002 Morocco Jerash Univ Dept Civil Engn Irbid Jordan Univ Fed Uberlandia Fac Civil Engn Campus Santa Mon Uberlandia MG Brazil Brunel Univ London Dept Civil & Environm Engn London England London South Bank Univ Sch Built Environm & Architecture Div Civil & Bldg Serv Engn London England Mutah Univ Coll Engn Civil & Environm Engn Dept Mutah 61710 Karak Jordan

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the crosssection resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), gene expression programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes.

关键词： Circular hollow sections Stainless steel Finite element model Artificial neural network Support vector machine regression gene expression programming Decision Trees for Regression

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Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

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CEMENT & CONCRETE COMPOSITES 2022年第0期133卷

作者： Salami, Babatunde Abiodun Iqbal, Mudassir Abdulraheem, Abdulazeez Jalal, Fazal E. Alimi, Wasiu Jamal, Arshad Tafsirojjaman, T. Liu, Yue Bardhan, Abidhan King Fand Univ Petr & Minerals Res Inst Interdisciplinary Res Ctr Construct & Bldg Mat Dhahran 31261 Saudi Arabia Univ Engn & Technol Dept Civil Engn Peshawar Pakistan Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai Key Lab Digital Maintenance Bldg & Infra State Key Lab Ocean Engn Shanghai 200240 Peoples R China King Fand Univ Petr & Minerals Coll Petr Engn & Geosci Dhahran 31261 Saudi Arabia King Fand Univ Petr & Minerals Coll Engn Civil & Environm Engn Dept Dammam 31261 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Engn Transportat & Traff Engn Dept POB 1982 Dammam 31451 Saudi Arabia Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA Australia Univ Southern Queensland USQ Ctr Future Mat CFM Sch Engn Toowoomba Qld 4350 Australia Univ Sci & Technol Beijing Res Inst Urbanizat & Urban Safety 30 Xueyuan Rd Beijing Peoples R China Natl Inst Technol NIT Civil Engn Dept Patna Bihar India

Foamed concrete is special not only in terms of its unique properties, but also in terms of its challenging compositional mixture design, which necessitates multiple experimental trials before obtaining the desired property like compressive strength. Regardless of design challenges, artificial intelligence (AI) techniques have shown to be useful in reliably estimating desired concrete properties based on optimized mixture proportions. This study proposes AI-based models to predict the compressive strength of foamed concrete. Three novel AI approaches, namely artificial neural network (ANN), gene expression programming (GEP), and gradient boosting tree (GBT) models, were employed. The models were developed using 232 experimental results, considering easily acquired variables, such as the density of concrete, water-cement ratio and sand-cement ratio as inputs to estimate the compressive strength of foamed concrete. In training the models, 80% of the experimental data was used and the rest was used to validate the models. The optimized models were selected using their respective best hyper-parameters on trial and error basis;variable number of hidden layers, number of neurons and training algorithms were used for ANN, number of chromosomes, head size, number of genes, variable function set for the GEP and GBT employed number of trees, maximal depth and learning rate. The trained models were validated using parametric and sensitivity analyses of a simulated dataset. The prediction abilities of proposed models were evaluated using the coefficient of correlation (R), mean absolute error (MAE), and root mean squared error (RMSE). For the validation data, empirical results from the performance evaluation revealed that GBT model (R = 0.977, MAE = 1.817 and RMSE = 2.69) has relative superior performance with highest correlation and least error in comparison with ANN (R = 0.975, MAE = 2.695 and RMSE = 3.40) and GEP (R = 0.96, MAE = 2.07 and RMSE = 2.80). The study concludes th

关键词： Foamed concrete Lightweight concrete Artificial neural network gene expression programming Gradient boosting tree Optimization

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Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 20卷

作者： Khan, Adil Khan, Majid Ali, Mohsin Khan, Murad Khan, Asad Ullah Shakeel, Muhammad Fawad, Muhammad Najeh, Taoufik Gamil, Yaser Univ Bradford Dept Adv Civil & Struct Engn Bradford BD7 1DP England COMSATS Univ Dept Civil Engn Islamabad 22060 Pakistan Southeast Univ Nanjing Sch Civil Engn Nanjing Peoples R China Tianjin Univ Sch Civil Engn Tianjin 300354 Peoples R China Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan Silesian Univ Technol Poland Gliwice Poland Budapest Univ Technol & Econ Hungary Budapest Hungary Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Operat Maintenance & Acoust Lulea Sweden Monash Univ Malaysia Sch Engn Dept Civil Engn Jalan Lagoon SelatanBandar Sunway Subang Jaya 47500 Selangor Malaysia

There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wea

关键词： Fly ash Abrasion resistance gene expression programming Multi-expression programming SHAP

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Mechanical features and durability of concrete incorporating recycled coarse aggregate and nano-silica: Experimental study, prediction, and optimization

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JOURNAL OF BUILDING ENGINEERING 2023年 73卷

作者： Rezaei, Farzad Memarzadeh, Armin Davoodi, Mohammad-Reza Dashab, Mohammad-Amin Nematzadeh, Mahdi Babol Noshirvani Univ Technol Dept Civil Engn Babol Iran Univ Mazandaran Fac Engn & Technol Dept Civil Engn Babolsar Iran

The mechanical features of concrete made with multiple quantities of recycled coarse aggregate replacing natural gravel were investigated in this work. In addition, behavior variations of con-crete specimens with recycled and natural coarse aggregates after incorporating colloidal nano-silica were addressed. For this purpose, 13 experimental groups and 195 specimens with different recycled coarse aggregate contents (0, 25, 50, 75, and 100%) and nano-silica (0, 1.5, 3, 4.5, and 6%) were produced. Then, key parameters including the compressive, splitting tensile, and flexural strengths, as well as ultrasonic pulse velocity (UPV), modulus of elasticity, water absorption, and porosity, were explored. Moreover, the test results were employed to propose empirical equations for the mechanical parameters of concrete with the recycled coarse aggregate and nano-silica contents as variables. The findings demonstrated a decline in the durability and mechanical characteristics by raising the quantity of recycled coarse aggregate replacing natural gravel. Furthermore, incorporating nano-silica in specimens containing recycled and those con-taining natural aggregate enhanced durability and mechanical features. Additionally, to deter-mine the optimum values of the design variables to maximize the mechanical features and durability of concrete incorporating recycled coarse aggregate and nano-silica, the response surface method (RSM) was used;optimum quantities of nano-silica and recycled coarse aggregate were determined as 4 and 26%, respectively. Finally, gene expression programming (GEP) was used to predict the compressive capacity of concretes incorporating recycled coarse aggregate and nano-silica pozzolan. The model was developed using 168 concrete specimens extracted from the literature and showed a good correlation between the results.

关键词： Recycled coarse aggregate Nano-silica Response surface method Optimization gene expression programming

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Optimizing compressive strength prediction models for rice husk ash concrete with evolutionary machine intelligence techniques

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CASE STUDIES IN CONSTRUCTION MATERIALS 2023年 18卷

作者： Amin, Muhammad Nasir Ahmad, Waqas Khan, Kaffayatullah Deifalla, Ahmed Farouk King Faisal Univ Coll Engn Dept Civil & Environm Engn Al Hasa 31982 Saudi Arabia COMSATS Univ Islamabad Dept Civil Engn Abbottabad 22060 Pakistan Future Univ Egypt Dept Struct Engn & Construct Management New Cairo 11835 Egypt

This research intended to increase the understanding of using modern machine intelligence techniques, including multi-expression programming (MEP) and gene expression programming (GEP), for the compressive strength (CS) prediction of rice husk ash (RHA) concrete. In addition, SHapley Additive exExplanations (SHAP) analysis was made to study the impact and interaction of raw materials on the CS of RHA concrete. A comprehensive database of 192 points with six inputs (cement, specimen age, RHA, superplasticizer, water, and fine aggregate) was used for developing prediction models. This research determined that both GEP and MEP models for the CS prediction of RHA concrete yielded reliable results, which were in close agreement with the real CS. Comparing the performance of both GEP and MEP models, it was noted that MEP, with an R2 of 0.89, outperformed the GEP model having an R2 of 0.83. Additionally, SHAP analysis indicated that specimen age was the most vital measure, followed by cement, which positively correlated with CS of RHA. The overall effect of RHA was found to be more positive, suggesting RHA utilization in the optimal range of 75-100 kg/m3 in the RHA concrete mix. The use of prediction models and SHAP analysis will help the building industry assess material properties and raw material effects faster and more economical.

关键词： Rice husk ash Sustainable concrete gene expression programming Multi -expression programming SHapley Additive exExplanations

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