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Soft computing models for assessing bond performance of reinforcing bars in concrete at high temperatures

INNOVATIVE INFRASTRUCTURE SOLUTIONS 2023年第8期8卷 1-19页

作者： Albostami, Asad S. Al-Hamd, Rwayda Kh. S. Alzabeebee, Saif Univ Petra Fac Engn Civil Engn Dept Amman Jordan Oryx Universal Coll Partnership Liverpool John Moo Sch Engn & Construct Doha Qatar Abertay Univ Sch Appl Sci Dundee Scotland Univ Al Qadisiyah Coll Engn Dept Rd & Transport Engn Al Diwaniyah Al Qadisiyah Iraq

The bond between steel and concrete in reinforced concrete structures is a multifaceted and intricate phenomenon that plays a vital role in the design and overall performance of such structures. It refers to the adhesion and mechanical interlock between the steel reinforcement bars and the surrounding concrete matrix. Under elevated temperatures, the bond is more complex under higher temperatures, yet having an accurate estimate is an important factor in design. Therefore, this paper focuses on using data-driven models to explore the performance of the concrete-steel bond under high temperatures using a gene expression programming (GEP) soft computing model. The GEP models are developed to simulate the bond performance in order to understand the effect of high temperatures on the concrete-steel bond. The results were compared to the multi-objective evolutionary polynomial regression analysis (MOGA-EPR) models for different input variables. The new model would help the designers with strength predictions of the bond in fire. The dataset used for the model was obtained from experiments conducted in a laboratory setting that gathered a 316-point database to investigate concrete bond strength at a range of temperatures and with different fibre contents. This study also investigates the impact of the different variables on the equation using sensitivity analysis. The results show that the GEP models are able to predict bond performance with different input variables accurately. This study provides a useful tool for engineers to better understand the concrete-steel bond behaviour under high temperatures and predict concrete-steel bond performance under high temperatures.

关键词： Concrete-Steel bond under High Temperatures gene expression programming Soft computing

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GEP-based models for estimating the elastic shear buckling and ultimate loads of cold-formed steel channels with staggered slotted web perforations in shear

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STRUCTURES 2022年 46卷 186-200页

作者： Ipek, Sueleyman Degtyarev, Vitaliy V. Guneyisi, Esra Mete Mansouri, Iman Bingol Univ Dept Architecture TR-12000 Bingol Turkey New Millennium Bldg Syst LLC Columbia SC 29204 USA Gaziantep Univ Dept Civil Engn TR-27310 Gaziantep Turkey Princeton Univ Dept Civil & Environm Engn Princeton NJ 08544 USA

Thermal bridging through cold-formed steel (CFS) channels can be substantially reduced by introducing stag-gered slots in the channels' webs. The perforation size and pattern significantly affect the elastic shear buckling and ultimate loads of the channels in shear, which must be considered in the design. This paper proposes new models for accurate estimations of the shear buckling and ultimate loads derived from an extensive database by employing gene expression programming (GEP). The model constructions involve various dimensional param-eters, types of boundary conditions, and the yield strength of the channels. The proposed GEP models, in the form of transparent mathematical expressions, provide reasonable prediction accuracy comparable to published descriptive equations. However, the GEP models are simpler than the existing equations and do not require additional advanced computations, as the existing equations do, to obtain the elastic shear buckling and ultimate shear loads. LRFD resistance and ASD safety factors for the proposed GEP model were determined in accordance with AISI S100-16 w/S2-20. Based on the presented information, the GEP models are recommended for esti-mating the elastic shear buckling and ultimate loads of CFS channels with staggered slotted web perforations in shear.

关键词： Cold-formed steel (CFS) gene expression programming Perforated web Shear buckling Shear strength

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Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques

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Journal of Building Pathology and Rehabilitation 2023年第2期8卷 1-15页

作者： Albostami, Asad S. Al-Hamd, Rwayda Kh. S. Alzabeebee, Saif School of Engineering & Construction Oryx Universal College in Partnership with Liverpool John Moores Doha Qatar Civil Engineering Department Faculty of Engineering University of Petra Amman Jordan School of Applied Sciences Abertay University Dundee United Kingdom Department of Roads and Transport Engineering College of Engineering University of Al-Qadisiyah Al-Qadisiyah Al Diwaniyah Iraq

This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a Multi-Objective genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and gene expression programming (GEP) models. The input variables considered are the longitudinal reinforcement ratio, recycled coarse aggregate ratio, beam cross-section dimensions, and concrete compressive strength. Data collected from the literature were used to train and validate the models. The results showed that the MOGA-EPR and GEP models can accurately predict the shear strength of beams without stirrups. The models also performed better than equations from the codes and literature. This study provides an alternative approach to accurately predict the shear strength of reinforced recycled aggregate concrete beams without stirrups. © 2023, The Author(s).

关键词： gene expression programming Multi-Objective genetic Algorithm Evolutionary Polynomial Regression Recycled aggregate concrete Shear strength of reinforced concrete Soft computing

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Estimating the deformation of micropile stabilized footings by GEP approach

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2023年第6期8卷 1-21页

作者： Malik, Bilal Ahmad Jalal, Fazal E. Iqbal, Mudassir Nabi, Sakiba Natl Inst Technol Dept Civil Engn Srinagar 190006 Jammu & Kashmir India Shanghai Jiao Tong Univ Dept Civil Engn Shanghai 200240 Peoples R China Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan

Micropiles possess a remarkable adaptability that allows them to function effectively as both a novel foundation and a sustainable solution for existing foundations, offering numerous advantages such as exceptional load-carrying capacity and minimal disturbance to the underlying subgrade. They have the potential to revive essential infrastructures, historic structures, or distressed structures. The primary objective of this investigation is to explore the in-situ reinforcement method, specifically vertical micropile reinforcement, for enhancing foundation performance. The research method employed involves a sequence of laboratory model tests on a square footing surrounded by micropiles, and subjected to vertical concentric loading. Additionally, the gene expression programming (GEP) technique was employed to model the micropile deformation, and by conducting numerous trials, the optimal trial yielded an R-2 of 0.9595 and a mean absolute error of 1.1135. The GEP hyperparameter tuning was achieved using trial and access, which indicated that the optimal head size was 10, the number of genes was 3, and the number of chromosomes was 200. The results indicate that 90% and 75% of the predicted values fall within the 20% relative error range for the training and validation data, respectively. Furthermore, a parametric analysis was conducted based on various geometrical and soil parameters to determine their impact. The results demonstrate that the placement of micropiles around a footing can significantly enhance its settlement characteristics and load-bearing capacity.

关键词： Micropiles Reinforcement gene expression programming Parametric analysis Prediction models

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gene expression programming strategy for estimation of flash point temperature of non-electrolyte organic compounds

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FLUID PHASE EQUILIBRIA 2012年 329卷 71-77页

作者： Gharagheizi, Farhad Ilani-Kashkouli, Poorandokht Farahani, Nasrin Mohammadi, Amir H. Islamic Azad Univ Dept Chem Engn Buinzahra Branch Buinzahra Iran Islamic Azad Univ Dept Chem Buinzahra Branch Buinzahra Iran MINES ParisTech CEP TEP Ctr Energet & Proc F-77305 Fontainebleau France Univ KwaZulu Natal Thermodynam Res Unit Sch Chem Engn ZA-4041 Durban South Africa

The accuracy and predictability of correlations and models to determine the flammability characteristics of chemical compounds are of drastic significance in various chemical industries. In the present study, the main focus is on introducing and applying the gene expression programming (GEP) mathematical strategy to develop a comprehensive empirical method for this purpose. This work deals with presenting an empirical correlation to predict the flash point temperature of 1471 (non-electrolyte) organic compounds from 77 different chemical families. The parameters of the correlation include the molecular weight, critical temperature, critical pressure, acentric factor, and normal boiling point of the compounds. The obtained statistical parameters including root mean square of error of the results from DIPPR 801 data (8.8, 8.9, 8.9 K for training, optimization and prediction sets, respectively) demonstrate improved accuracy of the results of the presented correlation with respect to previously-proposed methods available in open literature. (C) 2012 Elsevier B.V. All rights reserved.

关键词： gene expression programming Flammability characteristics Flash point genetic programming genetic algorithm

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Development and application of modeling techniques to estimate the unsaturated hydraulic conductivity

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2023年第4期9卷 4557-4571页

作者： Singh, Karan Singh, Balraj Sihag, Parveen Kumar, Vijendra Sharma, Kul Vaibhav Lovely Profess Univ Sch Civil Engn Phagwara 144411 Punjab India PIET Civil Engn Dept Panipat 132102 Haryana India Chandigarh Univ Dept Res & Dev Mohali 140413 Punjab India Dr Vishwanath Karad MIT World Peace Univ Sch Civil Engn Pune 411038 Maharashtra India

The knowledge of the unsaturated hydraulic conductivity (K) is very essential for the various fields of water resources, irrigation, and hydrology. It is also important to know the phenomena of water movement on the ground constantly. In order to better forecast unsaturated hydraulic conductivity, this article reports the comparison of efficacy of five distinct soft computing approaches: support vector machine (SVM), random forest, Gaussian process (GP), gene expression techniques, and multivariate adaptive regression spline. Three kernels function (Poly, RBF, and PUK) were used in SVM and GP modeling techniques. For fulfill this aim, experimentation has been performed using mini-disc infiltrometer in 20 locations in Ghaggar basin. Total 240 observations were collected, and out of which, 70% were used for training the model and remaining for testing. The input variables of this investigation were sand, clay, silt, bulk density (rho) and moisture content and output variable was K. The result of modeling techniques suggests that PUK kernel with SVM was superior to the other modeling techniques. This implies that these computational methods can be used to make estimates about the values of K at any time.

关键词： Gaussian process gene expression programming Hydraulic conductivity Multivariate adaptive regression spline Random forest Support vector machine

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Numerical and artificial intelligence models for predicting the water advance in border irrigation

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2022年第1期24卷 558-575页

作者： Emamgholizadeh, Samad Seyedzadeh, Amin Sanikhani, Hadi Maroufpoor, Eisa Karami, Gholamhosein Shahrood Univ Technol Dept Environm & Civil Engn Shahrood Iran Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran Univ Kurdistan Dept Water Engn Sanandaj Iran Shahrood Univ Technol Dept Geol Engn Shahrood Iran

The water advance time (Ta) is needed for designing and evaluating surface irrigation systems. This study employed artificial neural networks (ANNs) and gene expression programming (GEP) techniques for estimating the water advance time in the border irrigation system as a function of inflow rate per unit width (Q(b)), length of water advance in the border (L), longitudinal slope (S-o), final infiltration rate of the soil (f(o)) and Manning roughness coefficient (n). The techniques were tested on field measurements from agricultural farms in three different provinces of Iran. Results showed that the ANN model was superior to the GEP model for the estimation of water advance time. The performance indicators for the ANN model were R-2 = 0.966, RMSE = 7.805 min and MAE = 5.090 min, MBE = 0.312 and SI = 0.181. Results of the intelligence-based models were also compared with the WinSRFR model. Both ANN and GEP models predicted the water advance time more accurately than did the WinSRFR model.

关键词： gene expression programming Neural Networks Surface irrigation Water Advance Time WinSRFR

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Prediction of unsaturated soils effective stress parameter using gene expression programming

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SCIENTIA IRANICA 2013年第5期20卷 1433-1444页

作者： Johari, A. Nakhaee, M. Habibagahi, G. Shiraz Univ Technol Dept Civil & Environm Engn Shiraz Iran Shiraz Univ Dept Civil & Environm Engn Shiraz Iran

Unsaturated soil shear strength can be determined using effective stress relation that depends on the effective stress parameter. Several models have been developed in the past few years to estimate this parameter. In this research, the gene expression programming (GEP) is used as an Artificial Intelligence (Al) method for developing a model to predict the effective stress parameter, using efficient parameters. The principal advantage of the GEP approach is its ability to generate powerful prediction equations without any prior assumption on the possible form of the functional relationship. The input terminal set consists of net confining pressure, suction, Soil Water Characteristic Curve (SWCC) fitting parameter, bubbling pressure, residual and saturated volumetric water content. The output terminal set has one member, which is the effective stress parameter. An experimental database obtained from the literature is employed to develop the model. Comparison of the model prediction with the actual data, as well as other investigators, indicates a very good performance and ability of model. Sensitivity and parametric analyses are conducted to verify the results. It is also shown that soil suction is the most influential parameter in the effective stress parameter of unsaturated soils. (C) 2013 Sharif University of Technology. All rights reserved.

关键词： Unsaturated soil Effective stress parameter gene expression programming Soil suction

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gene expression programming based formulations for splitting tensile strength of concrete

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CONSTRUCTION AND BUILDING MATERIALS 2012年第1期26卷 404-410页

作者： Ozcan, Fatih Nigde Univ Dept Civil Engn TR-51240 Nigde Turkey

In this study, gene expression programming (GEP) formulations for splitting tensile strength (f(spt)) of the cylinder specimens with 150 mm diameter and 300 mm height using compressive strength (f(c)) of concrete cube specimens with 150 mm dimension are developed. Two models, called as GEP-I and GEP-II, are developed for predicting f(spt) by using GEP. The database used in the GEP models is based on experimental data obtained from literature. In the GEP-I model, while f(c) is used as input variable, f(spt) is used as output variable. However, in the GEP-II model, as well as f(c), water-binder ratio (WB) is used as input variables. The data sets used in training and testing stages are randomly selected among all experimental data. The GEP formulations are also validated with additional experimental data other than the data used in training and testing sets of the GEP models. Experimental f(spt) results of concrete specimens are compared with GEP formulations, proposed formulations by some national building codes and the developed regression-based formulation results. The results show that GEP formulations have strong potential as a feasible tool for prediction of the f(spt) from only 150 mm cube f(c) or WB and 150 mm cube f(c) of concrete. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Compressive strength Splitting tensile strength gene expression programming

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Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

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Theoretical & Applied Mechanics Letters 2024年第2期14卷 90-97页

作者： Hanyu Zhou Haochen Li Yaomin Zhao HEDPS Center for Applied Physics and Technologyand College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable *** the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior ***,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural *** networks are designed to represent explicit functional expressions and optimize them with data *** particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE *** proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

关键词： PDE discovery gene expression programming Deep Learning Knowledge embedding

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