Prediction model for the failure behavior of concrete under impact loading base on back propagation neural network

Ning, Jianguo; Feng, Yuanbao; Ren, Huilan; Xu, Xiangzhao

doi:10.1016/j.conbuildmat.2023.134297

Construction and Building Materials

2024

DOI: 10.1016/j.conbuildmat.2023.134297

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Prediction model for the failure behavior of concrete under impact loading base on back propagation neural network

Jianguo Ning,

Yuanbao Feng,

Huilan Ren

et al.

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Cited by 3 publications

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Study on hysteresis performance of four-limb CFST latticed column-box girder joints based on GA-BP neural network

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A diagnosis method for loss of circulation based on transient-pressure wave analysis and particle swarm optimization

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Lost circulation during operations poses a significant threat to production processes. In the search for an effective detection method, an impulse-response detection method of lost circulation is introduced. This method involves generating transient pressure waves at the wellhead and analyzing their time-frequency domain characteristics to pinpoint location for lost circulation within the wellbore annulus system. Utilizing the data processing capabilities of machine learning models, this study proposes an integrated model for signal feature classification and diagnosis model for lost circulation. Drawing from extensive experimental data, this model integrates laboratory experiments, signal analysis, and machine learning algorithms. Data preprocessing, including wavelet variation and denoising, precedes the application of an enhanced adaptive noise complete ensemble empirical modal decomposition with adapted noise (ICEEMDAN) alongside energy and sample entropy analysis for feature extraction. By establishing a mapping relationship between signal features and lost circulation changes, we develop an improved backpropagation neural network (IBP) model using the particle swarm optimization (PSO) algorithm for diagnosis (PSO-IBP). Comparative analysis of five models reveals compelling results: ① PSO-IBP achieves an average accuracy of 97.60%, with a standard deviation of 0.356; ② diagnosis accuracy for every lost circulation scenario exceeds 92%, outperforming other models in precision, recall, and F-Score; ③ even with limited training data, PSO-IBP maintains 84% accuracy, demonstrating superior performance. Further analysis highlights the efficacy of PSO-IBP, especially when leveraging ICEEMDAN for signal feature extraction, in accurately diagnosing lost circulation.

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Prediction model for the failure behavior of concrete under impact loading base on back propagation neural network

Cited by 3 publications

References 46 publications

Study on hysteresis performance of four-limb CFST latticed column-box girder joints based on GA-BP neural network

Study on hysteresis performance of four-limb CFST latticed column-box girder joints based on GA-BP neural network

Data-driven shear strength predictions of prestressed concrete hollow-core slabs

A diagnosis method for loss of circulation based on transient-pressure wave analysis and particle swarm optimization

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