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An improvement on fuzzy seismic fragility analysis using gene expression programming

STRUCTURAL ENGINEERING AND MECHANICS 2022年第5期83卷 577-591页

作者： Ebrahimi, Elaheh Abdollahzadeh, Gholamreza Jahani, Ehsan Babol Noshirvani Univ Technol Fac Civil Engn Babol Iran Univ Mazandaran Dept Civil Engn Babolsar Iran

This paper develops a comparatively time-efficient methodology for performing seismic fragility analysis of the reinforced concrete (RC) buildings in the presence of uncertainty sources. It aims to appraise the effectiveness of any variation in the material's mechanical properties as epistemic uncertainty, and the record-to-record variation as aleatory uncertainty in structural response. In this respect, the fuzzy set theory, a well-known alpha-cut approach, and the genetic Algorithm (GA) assess the median of collapse fragility curves as a fuzzy response. GA is requisite for searching the maxima and minima of the objective function (median fragility herein) in each membership degree, alpha. As this is a complicated and time-consuming process, the authors propose utilizing the gene expression programming-based (GEP-based) equation for reducing the computational analysis time of the case study building significantly. The results indicate that the proposed structural analysis algorithm on the derived GEP model is able to compute the fuzzy median fragility about 33.3% faster, with errors less than 1%.

关键词： fuzzy set theory gene expression programming genetic algorithm seismic fragility curve uncertainty

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A gene expression programming-based method for real-time wear estimation of disc cutter on TBM cutterhead

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NEURAL COMPUTING & APPLICATIONS 2022年第22期34卷 20231-20247页

作者： Tao, Jianfeng Yu, Honggan Qin, Chengjin Sun, Hao Liu, Chengliang Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Frequent entry in the tunnel boring machine cutterhead for disc cutter wear inspection is a risky, time-consuming, and labor-intensive activity. Existing disc cutter wear prediction models mainly focus on cutter consumption before construction, and it is impossible to estimate the wear of a single cutter when they are applied to on-site construction. To solve this problem, this research presents a method for estimating the wear of each disc cutter on the cutterhead in real time by only using several monitored machine parameters. Firstly, a novel health index that can characterize the wear of each disc cutter is constructed, and the field parameters that have greater impact on the health index are selected. Then, the explicit mathematical expression between the selected parameters and the health index is established based on genetic expression programming. Finally, the on-site data collected from an Indian subway tunnel were used to validate the effectiveness and superiority of the proposed method. The results show that the proposed method can estimate the wear of each disc cutter in real time only by monitoring the rotational speed of cutterhead and tunneling speed. Its average accuracies on the validation set and test set are 90.6% and 85.9%, respectively. Compared with the ridge regression, decision tree, support vector regression and k-nearest neighbors, its accuracy on the test set is 13.0%, 12.7%, 11.2%, and 15.4% higher, respectively. Therefore, the proposed method can greatly reduce the cost for cutter inspection, and the explicit model can be easily deployed to the construction site.

关键词： Tunnel boring machine Disc cutter Wear estimation Real time gene expression programming

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Prediction of compressive and splitting tensile strength of concrete with fly ash by using gene expression programming

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STRUCTURAL CONCRETE 2022年第4期23卷 2435-2449页

作者： Shah, Hammad Ahmed Rehman, Sardar Kashif Ur Javed, Muhammad Faisal Iftikhar, Yusra Cent South Univ Sch Civil Engn Changsha Peoples R China COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan

The mechanical properties of concrete are one of the most important properties in a design code. Accurate prediction models for mechanical properties are always desirable. Given environmental benefits, fly ash (FA) is often used as a replacement for Portland cement. In this study, two mechanical properties of concrete with FA, that is, compressive strength f 0 co THORN and splitting tensile strength f sto THORN were modeled by using gene expression programming (GEP). The GEP models were compared with response surface methodology, multiple linear and nonlinear regression. The sensitivity and parametric analysis were also performed. In addition, the influence of parameters of the GEP algorithm on the performance of the developed GEP models was evaluated by testing various linking functions, head sizes, number of genes, number of chromosomes, and fitness functions. It was revealed that the models developed by GEP have high predictive and generalization capability for both f 0 c and f st of concrete with FA as compared with the other modeling techniques.

关键词： compressive strength fly ash gene expression programming splitting tensile strength

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Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 3341-3350页

作者： Bayat, Parichehr Monjezi, Masoud Mehrdanesh, Amirhossein Khandelwal, Manoj Tarbiat Modares Univ Dept Min Fac Engn Tehran 14115143 Iran Federat Univ Australia Sch Engn Informat Technol & Phys Sci Ballarat Vic 3350 Australia

Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations.

关键词： Ground vibration Meta-heuristic optimization gene expression programming Grasshopper optimization algorithm

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Computation of energy dissipation across the type-A piano key weir by using gene expression programming technique

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WATER SUPPLY 2022年第8期22卷 6715-6727页

作者： Singh, Deepak Kumar, Munendra Delhi Technol Univ Dept Civil Engn Delhi India

Energy dissipation across the weir and dam structures is a vital economic and technical solution for designing the downstream morphology of any hydraulic system. Accurately estimating the energy over any hydraulic system using traditional empirical formulas is tedious and challenging. Consequently, employing new and precise techniques still in high demand is crucial. In this study, the authors developed an empirical model for estimating the residual energy downstream of the type-A PKW using gene-expression programming (GEP) by considering six non-dimensional parameters: headwater ratio, magnification ratio, inlet to outlet width ratio, inlet to outlet key bottom slopes, inlet to outlet overhang portions and the number of cycles. The performance of the proposed models has been compared to empirical equations using the statistical factors coefficient of determination (R-2), concordance coefficient (CC), and root mean square error (RMSE). The computed relative residual energy values using the proposed models are within +/- 10% of the observed ones. The proposed GEP model predicted the relative residual energy satisfactorily, with coefficients of determination (R-2 = 0.978 for training, 0.980 for testing) and root mean square errors (RMSE) of 0.032, 0.029 for the training and testing datasets, respectively.

关键词： energy dissipation gene expression programming low head efficient structure residual energy

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Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling

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WATER SCIENCE AND TECHNOLOGY 2022年第12期86卷 3205-3222页

作者： Lakshmi, Potharlanka Jhansi Apaza, Ruben Apaza Alkhayyat, Ahmed Marhoon, Haydar Abdulameer Alameri, Ameer A. A. Vignans Fdn Sci Technol & Res Comp Sci & Engn Guntur India Jose Maria Arguedas Natl Univ Acad Dept Engn & Informat Technol Andahuaylas Peru Islamic Univ Coll Tech Engn Najaf Iraq Al Ayen Univ Sci Res Ctr Informat & Commun Technol Res Grp Thi Qar Iraq Univ Kerbala Coll Comp Sci & Informat Technol Karbala Iraq Univ Babylon Coll Sci Dept Chem Babylon Iraq

It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an increase in the use of machine learning approaches to model rainfall-runoff. In this study, the accuracy of rainfall-runoff modeling approaches such as Support Vector Machine (SVM), gene expression programming (GEP), Wavelet-SVM (WSVM), and Wavelet-GEP (WGEP) is evaluated. Python is used to run the simulation. The research area is the Yellow River basin in central China, and in the west of the region, the Tang-Nai-Hai hydrometric station has been selected. The train state data ranges from 1950 to 2000, while the test state data ranges from 2000 to 2020. The analysis looks at two differ-ent types of rainy and non-rainy days. The WGEP simulation performed best, with a Nash-Sutcliffe efficiency (NSE) of 0.98, while the WSVM, GEP, and SVM simulations performed poorly, with NSEs of 0.94, 0.89, and 0.77, respectively. As a result, combining hybrid methods with Wavelet improved simulation accuracy, which is now the highest for the WGEP method.

关键词： gene expression programming machine learning rainfall-runoff modeling support vector machine wavelet

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Water surface profile in converging compound channel using gene expression programming

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WATER SUPPLY 2022年第5期22卷 5221-5236页

作者： Naik, Bandita Kaushik, Vijay Kumar, Munendra Methodist Coll Engn Dept Civil Engn Hyderabad India Delhi Technol Univ Dept Civil Engn Delhi India

Assessment of water surface profile in compound channels is essential for flood defence systems. Agriculture and development activities in floodplains affect the floodplain shape over the length, leading in a converging compound channel. Few laboratory investigations proved overbank flow in converging floodplains. Therefore, innovative and precise approaches are still in great demand. In this paper, new approach has been proposed to forecast the water surface profile of various compound channels with converging floodplains using gene expression programming (GEP). The models are constructed utilising pertinent experimental data from past studies. A new equation is devised to compute water surface profile in such channels using non-dimensional geometric and flow parameters such as converging angle, width ratio, relative distance, relative depth, aspect ratio and bed slope. The findings demonstrate that the GEP-derived water surface profile is in good correlation with the experimental data and data from other studies (R-2 = 0.99 and RMSE = 0.028 for the training data and R-2 = 0.99 and RMSE = 0.027 for the testing data). According to the results of statistically based investigations, the GEP model created for the study of compound channel flow is reliable and can be used in this domain.

关键词： converging compound channel error analysis flow parameters gene expression programming water surface profile

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A prediction model for strength and strain of CFRP-confined concrete cylinders using gene expression programming

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COMPUTERS AND CONCRETE 2022年第6期30卷 377-391页

作者： Alacali, Sema Yildiz Tech Univ Dept Civil Engn TR-34220 Istanbul Turkey

The use of carbon fiber-reinforced polymers (CFRP) has widely increased due to its enhancement in the ultimate strength and ductility of the reinforced concrete (RC) structures. This study presents a prediction model for the axial compressive strength and strain of normal-strength concrete cylinders confined with CFRP. Besides, soft computing approaches have been extensively used to model in many areas of civil engineering applications. Therefore, the genetic expression programming (GEP) models to predict axial compressive strength and strain of CFRP-confined concrete specimens were used in this study. For this purpose, the parameters of 283 CFRP-confined concrete specimens collected from 38 experimental studies in the literature were taken into account as input variables to predict GEP based models. Then, the results of GEP models were statistically compared with those of models proposed by various researchers. The values of R2 for strength and strain of CFRP-confined concrete were obtained as 0.897 and 0.713, respectively. The results of the comparison reveal that the proposed GEP-based models for CFRP-confined concrete have the best efficiency among the existing models and provide the best performance.

关键词： carbon fiber-reinforced polymers confined concrete gene expression programming strain strength

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Development of predictive models of asphalt pavement distresses in Idaho through gene expression programming

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NEURAL COMPUTING & APPLICATIONS 2022年第17期34卷 14913-14927页

作者： Deng, Yong Shi, Xianming Washington State Univ Dept Civil & Environm Engn Natl Ctr Transportat Infrastruct Durabil & Life E Pullman WA 99164 USA

Good predictive models should feature the capability of obtaining accurate results through easy and accessible methods in a timely manner. Following a "golden triangle" connecting the accuracy, complexity and applicability of each model, this work employed gene expression programming (GEP) algorithm to develop models for four typical distresses of asphalt pavements in the state of Idaho, respectively. The models developed in this work retain and combine advantages of empirical models and machine learning models and thus serve as promising alternatives to mechanistic-empirical (ME) models. Models with explicit forms were selected and calibrated by GEP, in an intelligent and automatic way. This work also proposes a variable selection method featuring principal component analysis coupled with correlation analysis, and this method was utilized in data pre-processing for construction of simple models with precise model form. Furthermore, this work introduces a systematic and comprehensive approach to evaluation of model accuracy, robustness and sensitivity, based on various statistical methods. Compared with regression models and ME models, the models generated by GEP achieved higher and more stable accuracy. Finally, we present the trade-off between model accuracy and rationality and point out the importance and necessity of reaching a balance in the golden triangle.

关键词： Asphalt pavement gene expression programming Golden triangle Accuracy Sensitivity Statistical method

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Experimental, Modeling, and Optimization Investigation on Mechanical Properties and the Crashworthiness of Thin-Walled Frusta of Silica/Epoxy Nano-composites: Fuzzy Neural Network, Particle Swarm Optimization/Multivariate Nonlinear Regression, and gene expression programming

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JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE 2022年第4期31卷 3030-3040页

作者： Dadrasi, A. Shariati, M. Farzi, Gh A. Fooladpanjeh, S. Islamic Azad Univ Dept Mech Engn Shahrood Branch Shahrood Iran Ferdowsi Univ Mashhad Fac Engn Dept Mech Engn Mashhad Razavi Khorasan Iran Hakim Sabzevari Univ Dept Mat & Polymer Engn Sabzevar *** Iran

In this work, an experimental study on the quasi-static collapse of thin-walled frusta of silica/epoxy nano-composites was conducted. The effect of nano-silica content and the particle size hybrid on the energy absorption capability of thin-walled frusta, the impact strength, Young's modulus, and the yield strength was investigated. For this purpose, three various sizes of the silica particle with the mean diameter of 17, 25, and 65 nm were used. The results showed that by adding the silica nano-particles up to 6 wt.%, the impact strength and Young's modulus increased, the yield strength remained constant, and the crashworthy capability of structures decreased. Also, two approaches including Fuzzy Neural Network, the hybrid of Particle Swarm Optimization (PSO), and Multivariate Nonlinear Regression (MNLR) were employed to determine the effect of the mentioned parameters. In comparison with the mentioned models and the experimental results, PSO/MNLR approach showed a better prediction for the parameters. Different parameters were optimized by gene expression programming. Some fracture surfaces were studied by scanning the electron microscopy.

关键词： crashworthy capability frusta gene expression programming particle swarm optimization silica nano-particles size hybrid

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