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Evaluation of the post fire mechanical strength properties of recycled aggregate concrete containing GGBS: optimization and prediction using machine learning techniques

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Asian Journal of Civil Engineering 2023年第6期24卷 1639-1666页

作者： Tung, Tran Minh Babalola, Olusola Emmanuel Le, Duc-Hien Sustainable Developments in Civil Engineering Research Group Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam

This study investigates the residual compressive strength and modulus of elasticity of recycled aggregate concrete containing ground granulated blast furnace slag (RAC-GGBS) exposed to high temperatures, presents the numerical optimization of the mix design parameters of RAC-GGBS to achieve maximized residual compressive strength, and develops prediction models for residual compressive strength of RAC-GGBS based gene expression programming (GEP) approach. The considered variables were the percentage replacement of RCA (50, 75, 100%), percentage replacement of GGBS (0, 20, 40, and 60%), water to binder ratio W/B (0.4 and 0.5), compressive strength (f_cu) and exposed temperatures of 25 ℃ (room temperature), 200, 400, 600, and 800 ℃. The experimental results show that addition of GGBS significantly enhanced the residual compressive strength and modulus of elasticity of RAC-GGBS while the SEM analysis conducted revealed a more compact microstructure with fewer pores and micro-cracks and better stabilized C–S–H hydrate formed at ITZ of RAC containing GGBS compared to RAC without GGBS after exposure to high temperature. The optimum condition obtained for RAC-GGBS exposed to high temperatures was 99.8%, 46.0% and 0.45 for RCA, GGBS and W/B, respectively, having maximized residual compressive strengths of 24.0, 25.5, 22.4, 17.2, 7.3 MPa after 25 ℃, 200 ℃, 400 ℃, 600 ℃ and 800 ℃ temperatures, respectively. The predictive ability of GEP model is high with R² values above 0.97 and low measured statistical errors. The mean absolute error (MAE) values of 0.889 and 1.244 and root mean square error (RMSE) of 1.064 and 1.502 for the training and validation datasets respectively, validate the GEP model's high accuracy and strong capacity to predict the residual compressive strength of RAC-GGBS. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： gene expression programming GGBS Recycled aggregate concrete Residual compressive strength Residual modulus of elasticity RSM optimization

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

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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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Prediction of deformations of steel plate by GEP in forming process

Prediction of deformations of steel plate by GEP in forming ...

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3rd International Conference on Advances in Product Development and Reliability (PDR 2012)

作者： Yang, Lei Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9783037854501

Line heating is the main method for forming compound curved shells of hull. The accuracy of final deformation and the productivity depend on the experience of the workers. To predict the plate deformation, the explicit mathematical model for deformation and the main influencing factors by FEA and GEP is established in this paper. The main influencing factors in line heating process were analyzed firstly. Then, 16 group deformation results of steel plate under the five main influencing factors were obtained by FEA. At last, the explicit mathematical model for deformation and the main influencing factors was established.

关键词： Line heating deformation Finite Element Method gene expression programming

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A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded expression programming 28th

A Novel Neural Network-Based Symbolic Regression Method: Neu...

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28th International Conference on Artificial Neural Networks (ICANN)

作者： Anjum, Aftab Sun, Fengyang Wang, Lin Orchard, Jeff Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9783030304843;9783030304836

Neuro-encoded expression programming (NEEP) that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. genetic programming with linear representation uses nature-inspired operators (e.g., crossover, mutation) to tune expressions and finally search out the best explicit function to simulate data. The encoding mechanism is essential for genetic programmings to find a desirable solution efficiently. However, the linear representation methods manipulate the expression tree in discrete solution space, where a small change of the input can cause a large change of the output. The unsmooth landscapes destroy the local information and make difficulty in searching. The neuro-encoded expression programming constructs the gene string with recurrent neural network (RNN) and the weights of the network are optimized by powerful continuous evolutionary algorithms. The neural network mappings smoothen the sharp fitness landscape and provide rich neighborhood information to find the best expression. The experiments indicate that the novel approach improves training efficiency and reduces test errors on several well-known symbolic regression problems.

关键词： genetic programming Symbolic regression Neural network gene expression programming Evolutionary algorithm

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Forecasting daily stream flows using artificial intelligence approaches

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ISH Journal of Hydraulic Engineering 2012年第3期18卷 204-214页

作者： Shiri, Jalal Kişi, Özgur Makarynskyy, Oleg Shiri, Abbas-Ali Nikoofar, Bagher Sama Technical and Vocational Training College Tabriz Branch Islamic Aazd University Tabriz Iran Civil Engineering Department Architectural and Engineering Faculty Canik Basari University Samsun Turkey URS Australia Brisbane Australia Ministry of Road and Urbanization Road Maintenance and Transportation Organization Tabriz Iran Department of Civil Engineering Ajabshir Branch Islamic Azad University Ajabshir Iran

This study compares three different artificial intelligence approaches, namely, gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs), in daily stream flow forecasting of Alavian Dam Station on the Soofi-Cahi River in the Northwestern Iran. The study demonstrates that the optimal results were obtained from the triple-input models, including stream flows of current and 2 previous days, and that the GEP model performed better than the ANN and ANFIS models in daily stream flow forecasting. It was found that the optimal GEP model with coefficient of determination R ²= 0.924, root mean square error = 0.908 m³/s and scatter index = 0.354 in the test stage was superior in forecasting daily stream flow to the optimal ANN and ANFIS models. © 2012 Taylor & Francis Group, LLC.

关键词： Forecasting gene expression programming Neural networks Neuro-fuzzy

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Adaptive Weighted-function Models for Time Series Prediction 11

Adaptive Weighted-function Models for Time Series Prediction

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11th World Congress on Intelligent Control and Automation

作者： Liu, Julie Yu-Chih Yuliani, Asri Rizki Wu, Chia-Ling Yuaan Ze Univ Innovat Ctr Big Data & Digital Convergence IM Dept 135 Yuan Tung Rd Tayuan 32003 Taiwan Yuaan Ze Univ Dept Informat Management Tayuan 32003 Taiwan

ISBN: (纸本)9781479958252

Time series prediction has been widely used in various fields. GEP is one of the popular methods for time series analysis. However, the GEP-based prediction models contain only one single function. To accurately capture the dynamic behavior of time series, this study develops a system which integrates multiple functions in a GEP-based model for time series prediction. The weight of each function is determined by the accuracy of its last prediction. In addition, a light local search is applied to adjust the function weights. The experimental results show that the proposed system outperforms several GEP-based approaches.

关键词： gene expression programming time series prediction weighted function symbolic regression

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Self-organized and Evolvable Cognitive Architecture for Intelligent Agents and Multi-Agent Systems

Self-organized and Evolvable Cognitive Architecture for Inte...

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2nd International Conference on Computer Engineering and Applications

作者： Javier, Oscar Lopez, Romero Fdn Univ Konrad Lorenz Software Engn Dept Cra 9 Bis 62-43 Bogota Colombia

ISBN: (纸本)9780769539829

Managing and arbitrating behaviours, processes and components in multilayered cognitive architectures when a huge amount of environmental variables are changing continuously with increasing complexity, ensue in a very comprehensive task. The presented framework proposes an hybrid cognitive architecture that relies on subsumption theory and includes some important extensions. These extensions can be condensed in inclusion of learning capabilities through bio-inspired reinforcement machine learning systems, an evolutionary mechanism based on gene expression programming to self-configure the behaviour arbitration between layers, a co-evolutionary mechanism to evolve behaviour repertories in a parallel fashion and finally, an aggregation mechanism to combine the learning algorithms outputs to improve the learning quality and increase the robustness and fault tolerance ability of the cognitive agent. The proposed architecture was proved in an animat environment using a multi-agent platform where several learning capabilities and emergent properties for self-configuring internal agent's architecture arise.

关键词： Intelligent Agents Cognitive Architectures Evolutionary Approaches Artificial Immune Systems gene expression programming

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Tailored excitations for structural health monitoring via evolutionary programming

Tailored excitations for structural health monitoring via ev...

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Conference on Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems V

作者： Olson, Colin C. Todd, M. D. Univ Calif San Diego Dept Struct Engn La Jolla CA 92093 USA

ISBN: (纸本)0819462306

Dynamic interrogation of structures for the purposes of damage identification is an active area of research within the field of structural health monitoring with recent work focusing on the use of chaotic excitations and state-space analyses for improved damage detection. Inherent in this overall approach is the specific interaction between the chaotic input and the structure's eigenstate. The sensitivity to damage is theoretically enhanced by special tailoring of the input in terms of stability interaction with the structure. This work outlines the use of an evolutionary program to search the parameter space of a chaotic excitation for those parameters that are best suited to appropriately couple the excitation with the structure for enhanced damage detection. State-space damage identification metrics are used to detect damage in a computational model driven by excitations produced via the evolutionary program with non-optimized excitations used as comparison cases.

关键词： structural health monitoring chaos evolutionary programming gene expression programming

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An Improved GEP-GA Algorithm and Its Application

An Improved GEP-GA Algorithm and Its Application

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6th International Symposium on Intelligence Computation and Applications (ISICA 2012)

作者： Yao, Lei Li, Hui China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

ISBN: (纸本)9783642342882

GEP is a powerful tool for automatically function modeling. However, the classical GEP have some appearances such as lack of learning mechanism, search blindly, lack of diversity, prone to precocity when dealing with complicate problems. In light of these limitations the Improved GEP-GA Algorithm introduces the uniform initial population strategy, the adaptive mutation, the variation of population size strategy based on stagnant generations, and optimizes the coefficient of model by GA after the work of GEP. Then it proved that the Improved GEP-GA Algorithm is more effective than other similar algorithms in modeling and forecast though some experiments. It will make the algorithm hard to get into the local trap, and improve the fitting efficiency and forecasting accuracy of mining. The result of that applied the improved GEP-GA algorithm to the relay's parameter design shows that it will save time in calculating the electromagnetic suction force and improve the computational efficiency in a large degree. It has a broad apace for development and application in relay parameters design.

关键词： gene expression programming generic Algorithms Relay Electromagnetic Suction Force Prediction

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Novel Data Mining Techniques in aCGH based Breast Cancer Subtypes Profiling: the Biological Perspective

Novel Data Mining Techniques in aCGH based Breast Cancer Sub...

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IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

作者： Menolascina, F. Tommasi, S. Paradiso, A. Cortellino, M. Bevilacqua, V Mastronardi, G. Natl Canc Inst Clin & Expt Oncol Lab I-70126 Bari Italy Univ Bari Dept Elect & Elect Engn I-70125 Bari Italy

ISBN: (纸本)9781424407101

In this paper we present a comparative study among well established data mining algorithm (namely J48 and Naive Bayes Tree) and novel machine learning paradigms like Ant Miner and gene expression programming. The aim of this study was to discover significant rules discriminating ER+ and ER-cases of breast cancer. We compared both statistical accuracy and biological validity of the results using common statistical methods and gene Ontology. Some worth noting characteristics of these systems have been observed and analysed even giving some possible interpretations of findings. With this study we tried to show how intelligent systems can be employed in the design of experimental pipeline in disease processes investigation and how deriving high-throughput results can be validated using new computational tools. Results returned by this approach seem to encourage new efforts in this field.

关键词： ant miner breast caner decision trees gene expression programming rule induction

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