Deep Learning-supported Case-Based Reasoning for Prevention and Treatment of Construction Project Quality Problems
To address ineffective decision-making and utilization in the quality management process of construction projects in a large number of historical cases,a case-based reasoning(CBR)model supported by deep learning is proposed.Historical cases are collected and analyzed.Their feature attributes are extracted based on the regular expression and bidirectional encoder representations from the transformers-bidirectional long short-term memory-conditional random field(BERT-BiLSTM-CRF)model.This model can achieve a more automatic construction of the case database.The BERT algorithm is introduced to improve the similarity measure of CBR.A prevention and treatment approach is recommended for the target case through case retrieval matching and modification and is validated with a real-life case.The results show that this approach can recognize similar cases from the case database,enhancing the efficiency and accuracy of quality management decision-making.
construction project qualitydeep learningBERTcase-based reasoning
NETL
NSTL
万方数据
