An intelligent discovery method for design formulas in civil engineering
Al-based computation in civil engineering exhibits high accuracy and efficiency.However,due to its black-box nature,the results are difficult for researchers and engineers to comprehend,impeding its application in practical engineering projects that prioritize safety.To address this issue,a design formula intelligent discovery method based on dimensional analysis and engineering prior knowledge is proposed.This method utilizes intelligent computing technology to automatically identify the key features affecting the performance of materials and components from experimental data and generate design formulas that are dimensionally balanced,physically meaningful,and mechanically interpretable.A formula intelligent generation model considering dimensional constraints is established based on symbolic regression expression trees,ensuring the mechanical rationality of the formulas.Normalization methods for scenarios with multiple mechanical-geometric variables and engineering feature segmentation algorithms based on spectral clustering and decision trees are developed to further improve the stability and accuracy of the model.The effectiveness of the method is verified using the shear bearing capacity of reinforced cementitious materials as an example.The results show that the intelligent-generated formulas improve the accuracy by 61.3％and the fitting correlation by 23.3％compared to empirical formulas generated manually,with R2 value of 0.90,demonstrating excellent performance.Moreover,compared to traditional symbolic regression methods,the intelligent-generated formulas are not only more accurate but also dimensionally correct,with stronger engineering generalization capabilities.Furthermore,the proposed method contributes to revealing the mechanical mechanisms and accelerating the translation process from experimental testing to design methods for new materials and structures.
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