Progress in Artificial Intelligence-based Prediction of Concrete Performance
Source abstract
Abstract
Artificial intelligence technology has super high-dimensional nonlinear computing capabilities, intelligent comprehensive analysis and judgment functions, and self-learning knowledge reserve expression functions. It can unlock the potential of high-dimensional nonlinearity relation between tangible components and performance indicators when compared to the empirical formula generated from classic statistical approaches. This article summarizes the types of artificial intelligence algorithms used to predict concrete performance, comprehensively sorts out the research progress of artificial intelligence technology in predicting the mechanical properties, work performance, and durability of concrete, and compares and analyzes the effects of algorithm selection, sample data, and model construction on the concrete compressive prediction system. The analysis shows that artificial intelligence technology has obvious advantages in measurement accuracy in predicting concrete performance compared to conventional statistical methods. Multiple algorithms should be used to cross-validate the model prediction findings. For tiny data sets, support vector machines are utilized. Decision tree evolution techniques should be used in algorithm models that require feature optimization or dispersed index prediction. Artificial neural networks can be used to solve different challenges. To improve the prediction model and boost its prediction accuracy, measures such as optimized features, integrated algorithms, hyperparameter optimization, enlarged sample data set, richer data sources, and data pretreatment are proposed.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · BIM and Construction Integration · AI and Multimedia in Education
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Architecture and environment, Sichuan University, Chengdu 610065, China.
Shanghai Jiao Tong University, Shanghai 200240, China.
References (85)
Show source-provided references
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