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Prediction of Ultra-High-Performance Concrete (UHPC) Properties Using gene expression programming (GEP)

BUILDINGS 2024年第9期14卷 2675页

作者： Qian, Yunfeng Yang, Jianyu Yang, Weijun Alateah, Ali H. Alsubeai, Ali Alfares, Abdulgafor M. Sufian, Muhammad Changsha Univ Sci & Technol Sch Civil Engn Changsha 410000 Hunan Peoples R China Univ Hafr Al Batin Coll Engn Dept Civil Engn Hafar Al Batin 39524 Saudi Arabia Royal Commiss Jubail Jubail Ind Coll Dept Civil Engn Jubail Industrial City 31961 Saudi Arabia Univ Hafr Al Batin Coll Engn Dept Elect Engn Hafar al Batin 39524 Saudi Arabia Southeast Univ Sch Civil Engn Nanjing 210096 Peoples R China

In today's digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (mu m and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m3). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m3. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R2, i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the cha

关键词： ultra-high-performance concrete flowability compressive strength flexural strength porosity prediction model gene expression programming graphical user interface

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Feature selection approach for evolving reactive scheduling policies for dynamic job shop scheduling problem using gene expression programming

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第15期61卷 5029-5052页

作者： Shady, Salama Kaihara, Toshiya Fujii, Nobutada Kokuryo, Daisuke Kobe Univ Grad Sch Syst Informat Kobe Hyogo Japan

Dispatching rules are one of the most widely applied methods for solving Dynamic Job Shop Scheduling problems (DJSSP) in real-world manufacturing systems. Hence, the automated design of effective rules has been an important subject in the scheduling literature for the past several years. High computational requirements and difficulty in interpreting generated rules are limitations of literature methods. Also, feature selection approaches in the field of automated design of scheduling policies have been developed for the tree-based GP approach only. Therefore, the aim of this study is to propose a feature selection approach for the gene expression programming (GEP) algorithm to evolve high-quality rules in simple structures with an affordable computational budget. This integration speeds up the search process by restricting the GP search space using the linear representation of the GEP algorithm and creates concise rules with only meaningful features using the feature selection approach. The proposed algorithm is compared with five algorithms and 30 rules from the literature under different processing conditions. Three performance measures are considered including total weighted tardiness, mean tardiness, and mean flow time. The results show that the proposed algorithm can generate smaller rules with high interpretability in a much shorter training time.

关键词： Feature selection gene expression programming dispatching rules genetic programming dynamic job shop scheduling discrete event simulation

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Automatic Design of a Hyper-Heuristic Framework With gene expression programming for Combinatorial Optimization Problems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2015年第3期19卷 309-325页

作者： Sabar, Nasser R. Ayob, Masri Kendall, Graham Qu, Rong Univ Kebangsaan Malaysia Data Min & Optimizat Res Grp Bangi 43600 Malaysia Univ Nottingham Semenyih 43500 Malaysia Univ Nottingham ASAP Res Grp Nottingham NG8 1BB England Univ Nottingham ASAP Res Grp Sch Comp Sci Nottingham NG8 1BB England

Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high-level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems, or even instances, have different landscape structures and complexity, the design of efficient high-level heuristics can have a dramatic impact on hyper-heuristic performance. In this paper, instead of using human knowledge to design the high-level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance-solving process, the high-level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low-level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different high-level heuristics during the problem-solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism that contains a population of both high-quality and diverse solutions that is updated during the problem-solving process. The generality of the proposed hyper-heuristic is validated against six well-known combinatorial optimization problems, with very different landscapes, provided by the HyFlex software. Empirical results, comparing the proposed hyper-heuristic with state-of-the-art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains.

关键词： Dynamic optimization gene expression programming hyper-heuristics timetabling vehicle routing

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Empirical modeling of shear strength of steel fiber reinforced concrete beams by gene expression programming

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NEURAL COMPUTING & APPLICATIONS 2013年第3-4期23卷 823-834页

作者： Kara, Ilker Fatih Nigde Univ Dept Civil Engn Nigde Turkey

The addition of steel fibers into concrete improves the postcracking tensile strength of hardened concrete and hence significantly enhances the shear strength of reinforced concrete reinforced concrete beams. However, developing an accurate model for predicting the shear strength of steel fiber reinforced concrete (SFRC) beams is a challenging task as there are several parameters such as the concrete compressive strength, shear span to depth ratio, reinforcement ratio and fiber content that affect the ultimate shear resistance of FRC beams. This paper investigates the feasibility of using gene expression programming (GEP) to create an empirical model for the ultimate shear strength of SFRC beams without stirrups. The model produced by GEP is constructed directly from a set of experimental results available in the literature. The results of training, testing and validation sets of the model are compared with experimental results. All of the results show that GEP model is fairly promising approach for the prediction of shear strength of SFRC beams. The performance of the GEP model is also compared with different proposed formulas available in the literature. It was found that the GEP model provides the most accurate results in calculating the shear strength of SFRC beams among existing shear strength formulas. Parametric studies are also carried out to evaluate the ability of the proposed GEP model to quantitatively account for the effects of shear design parameters on the shear strength of SFRC beams.

关键词： Fiber reinforced concrete gene expression programming Shear strength

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A hybrid gene expression programming algorithm based on orthogonal design

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2016年第4期9卷 778-787页

作者： Yang, Jie Ma, Jun Univ Wollongong Fac Engn & Informat Sci SMART Infrastruct Facil Northfields Ave Wollongong NSW 2522 Australia

The last decade has witnessed a great interest on the application of evolutionary algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) and gene expression programming (GEP), for optimization problems. This paper presents a hybrid algorithm by combining the GEP algorithm and the orthogonal design method. A multiple-parent crossover operator is introduced for the chromosome reproduction using the orthogonal design method. In addition, an evolutionary stable strategy is also employed to maintain the population diversity during the evolution. The efficiency of the proposed algorithm is evaluated using three benchmark problems. The results demonstrate that the proposed hybrid algorithm has a better generalization ability compared to conventional algorithms.

关键词： Evolutionary computation gene expression programming Orthogonal design Evolutionary stable strategy

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Enhancing gene expression programming based on space partition and jump for symbolic regression

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INFORMATION SCIENCES 2021年 547卷 553-567页

作者： Lu, Qiang Zhou, Shuo Tao, Fan Luo, Jake Wang, Zhiguang China Univ Petr Beijing Key Lab Petr Data Min Beijing Peoples R China Univ Wisconsin Dept Hlth Sci & Adm Milwaukee WI 53201 USA

When solving a symbolic regression problem, the gene expression programming (GEP) algorithm could fall into a premature convergence which terminates the optimization pro-cess too early, and may only reach a poor local optimum. To address the premature convergence problem of GEP, we propose a novel algorithm named SPJ-GEP, which can maintain the GEP population diversity and improve the accuracy of the GEP search by allowing the population to jump efficiently between segmented subspaces. SPJ-GEP first divides the space of mathematical expressions into k subspaces that are mutually exclusive. It then creates a subspace selection method that combines the multi-armed bandit and the E greedy strategy to choose a jump subspace. In this way, the analysis is made on the population diversity and the range of the number of subspaces. The analysis results show that SPJ-GEP does not significantly increase the computational complexity of time and space than classical GEP methods. Besides, an evaluation is conducted on a set of standard SR benchmarks. The evaluation results show that the proposed SPJ-GEP keeps a higher population diversity and has an enhanced accuracy compared with three baseline GEP methods. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Symbolic regression gene expression programming genetic programming Multi-armed bandit Evolutionary computation

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Impact of fine lightweight aggregates and coal waste on structural lightweight concrete: Experimental study and gene expression programming

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STRUCTURES 2024年 63卷

作者： Mijan, Reza Sanjari Momeni, Mohammad Hadianfard, Mohammad Ali Shiraz Univ Technol Dept Civil & Environm Engn Shiraz Iran Univ Trieste Dept Engn & Architecture Trieste Italy

Light Expanded Clay Aggregate (LECA) is one of the materials used to make lightweight concrete. In this study, two types of lightweight aggregates including LECA in both coarse and fine aggregate forms and pumice in fine aggregate form have been used to produce lightweight aggregate concrete. Coal waste is also used as partial replacement for cement. The results show that the usage of pumice as partial replacement for sand and LECA as coarse aggregate is applicable and can used for producing structural lightweight aggregate concrete. Furthermore, by using and increasing in the amount of LECA as fine aggregate, the slump of lightweight concrete in comparison to using sand, is also increased. The mixtures with LECA in both fine and coarse aggregate forms have the best results. Also, the results show that adding the coal waste improves the properties of lightweight concrete, and the optimal ratio is about 15% as partial replacement for cement by weight. In addition, gene expression programming is employed to establish empirical connections derived from experimental outcomes. In this regard, a relationship is proposed to determine the compressive strength of lightweight aggregate concrete based on the experimental data. The outcomes of gene expression programming demonstrate that the suggested formula exhibits good accuracy and efficiency in forecasting the specified parameter.

关键词： Lightweight aggregate concrete Coal waste LECA Pumice Compressive strength gene expression programming

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Modeling and monitoring the material removal rate of abrasive belt grinding based on vision measurement and the gene expression programming (GEP) algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第1-2期120卷 385-401页

作者： Ren, Lijuan Wang, Nina Pang, Wanjing Li, Yongchang Zhang, Guangpeng Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China

Accurately predicting the material removal rate (MRR) in belt grinding is challenging because of the randomly distributed multiple cutting edges, flexible contact, and continuous wear of the abrasive grains, undermining the ability to achieve the expected machining requirements for belt grinding using the planned parameters. With the development of sensing technology, big data, and intelligent algorithms, online identification methods for material removal through sensing signals have gained traction. A vision-based material removal monitoring method in the belt grinding process was investigated by adopting the gene expression programming (GEP) algorithm. First, the relationship between the grinding parameters and MRR was investigated through a series of experiments. Second, methods of image shooting distance calibration and automatic image segmentation were established. Furthermore, the definition and quantification method of 11 features related to the color, texture, and energy of spark images are described, based on which the features are extracted. Then, the optimal feature subset was determined by analyzing the fluctuation degree and correlation with MRR by computing the coefficient of variation of the features and Pearson's coefficient of features and MRR, respectively. Finally, a continuous function model including the selected features was obtained using the GEP method. The predicted results and testing time were compared with those of other methods such as LightGBM, convolutional neural network (CNN), support vector regression (SVR), and BP neural network. The results show that the MRR prediction model based on the GEP algorithm can obtain explicit function expressions and is highly effective in predicting accuracy and test time, which is of utmost significance for accurate and efficient acquisition of MRR data online.

关键词： Belt grinding Material removal rate Vision-based online monitoring gene expression programming

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New gene expression programming models for normalized shear modulus and damping ratio of sands

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015年 45卷 464-472页

作者： Keshavarz, Amin Mehramiri, Mohammad Persian Gulf Univ Sch Engn Bushehr Iran

As the most important dynamic properties of soils, shear modulus and damping ratio are two parameters employed to solve problems including seismic site response evaluation, dynamic analyses and equivalent-linear models. The work presented in this paper proposes two models for evaluation of the normalized shear modulus and two additional models for evaluation of the damping ratio of sands through gene expression programming (GEP). The data used in the modeling entails the valid experimental results obtained from previous researchers. As compared to the secondary models, the first two models are more accurate with larger equation length. The parameters taken into account as model inputs consisted of shear strain, mean effective confining pressure, and void ratio. In order to evaluate the performance and accuracy, the proposed models were processed through several statistical measures such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and coefficient of determination (R-2). Furthermore, the relative difference between predicted and measured values was calculated, which suggested that the models were desirably accurate. Finally, the model outputs were compared against other studies, the results of which demonstrated that the proposed models are capable of estimating the dynamic parameters of sands more accurately. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Normalized shear modulus Damping ratio gene expression programming Sands

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Runoff forecasting using hybrid Wavelet gene expression programming (WGEP) approach

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JOURNAL OF HYDROLOGY 2015年 527卷 326-344页

作者： Shoaib, Muhammad Shamseldin, Asaad Y. Melville, Bruce W. Khan, Mudasser Muneer Univ Auckland Dept Civil & Environm Engn Auckland 1 New Zealand Bahauddin Zakariya Univ Dept Civil Engn Multan Pakistan

This study presents a novel approach of using the hybrid Wavelet gene expression programming (WGEP) model to forecast the runoff using rainfall data. The rainfall-runoff data from four different catchments located in the different Hydro-Climatic regions of the world is used in the study. The WGEP model is developed by integrating the discrete wavelet transform (DWT) with the gene expression programming (GEP) models so that individual strengths of each approach can be exploited in a synergistic manner. It is implemented by transforming the input rainfall data using the DWT in order to reveal the temporal and the spectral information contained in the data and subsequently this transformed data is used as input for the GEP models. Ten different mother wavelet functions from different wavelet families are employed in the study in order to transform the data. The mother wavelet functions used in the study include the simple mother wavelet function Haar, the db2, db4, db8 wavelet functions of the most popular wavelet family Daubechies, sym2, sym4, sym8 wavelet functions with sharp peaks, coif2, coif4 wavelet functions and the discrete approximation of meyer (dmey) wavelet. The study further investigates the selection of the optimum input vector to be used in conjunction with the WGEP models by considering nine different input vectors. The first five input vectors contain only one rainfall data series ranging from lagged-1 day to lagged-5 day rainfall data series. The remaining four input vectors are selected using the most common approach in which selection of the input vector comprising of the sequential time series data. The performance of the hybrid WGEP models are compared with the simple GEP models developed without the wavelet transformation for the four selected catchments. The study found that the performance of the WGEP models is superior relative to the simple GEP model for all the nine input vectors considered. However, the WGEP models outperformed their respec

关键词： Rainfall-runoff modelling gene expression programming Wavelet transformation

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