Neural network model to predict compressive strength of steel fiber reinforced concrete elements incorporating supplementary cementitious materials

Gehlot, Tarun; Dave, Mayank; Solanki, Deepanshu

doi:10.1016/j.matpr.2022.04.327

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Zheng et al used decision tree, SVR and ANN to construct the performance prediction model of silica fume concrete, and they also used the bagging and boosting methods to assemble the above ML algorithms [19]. After reviewing the recent literature, it was found that the ANN-based modulus is the most wildly used machine learning algorithm for the performance prediction of concrete regarding recycled aggregate concrete [20,21], high-performance concrete [17,22,23], foamed concrete [24][25][26], metakaolin-based concrete materials [27], self-compacted concrete [28], rubberized concrete [29], concrete slabs [30] and other concrete accessories such as steel tubes [31,32], FRP bars [33][34][35][36][37], steel bars [38] and concrete blocks [39].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms

Yang,

Liu,

Zhang

et al. 2024

Buildings

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Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS) were selected as regression and prediction algorithms in this study; the particle swarm optimization (PSO) algorithm was also used to optimize the structure and hyperparameters of each algorithm. The statistical results show that the performance of the assembled algorithms is better than that of an NN-based algorithm. In addition, PSO can effectively improve the prediction accuracy of the ML algorithms. The comprehensive performance of each model is analyzed using a Taylor diagram, and the PSO-XGBoost model has the best comprehensive performance, with R2 and MSE equal to 0.9072 and 11.4546, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms

Yang,

Liu,

Zhang

et al. 2024

Buildings

View full text Add to dashboard Cite

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review

Kazemi,

Shafighfard,

Yoo

2024

Arch Computat Methods Eng

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Neural network model to predict compressive strength of steel fiber reinforced concrete elements incorporating supplementary cementitious materials

Cited by 5 publications

References 12 publications

Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms

Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review

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