Development of an AI-Powered System for Anticipating Displacement in Deep Excavation
Keywords:
extensive excavation, AI technology, wall shift, advanced functional-link networkAbstract
This paper outlines a novel methodology driven by artificial intelligence to predict the displacement of retaining walls during extensive deep excavation activities. In our selection of 17 training cases, we deliberately opted for a wall configuration unaffected by corner effects. This deliberate choice was made to guarantee symmetrical support for each deep excavation instance in our study, thus simplifying the analysis in subsequent phases. Our proposed multilayer functional-link network exhibits superior performance compared to the traditional backpropagation neural network (BPNN), excelling in accurately predicting displacements at specified observation points, peak wall displacements, and their corresponding locations. Noteworthy is the fact that the predictive accuracy of our advanced model exceeded that of the conventional BPNN and RIDO assessment tools by a significant 5%. The network process model developed in this research provides a valuable guide for future implementations in various geographic contexts. Moreover, through the utilization of local datasets in the training, testing, and validation phases, our system ensures the efficient and precise execution of displacement predictions.
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