Research Based On Natural Language Processing For Risk Of Construction Accident Reports

As one of the core goals of construction project management,safety manegement is vital in the construction industry.Text reports of construction accidents contain much information about the accident situation.However,past researches on construction risks seldom cooperate with reports of accident cases to summarize safety management experience.Thus,it seems necessary to develop comprehensive analysis methods to explore a relied tool and dig diverse risky factors' influences on safety accidents from text reports.Based on the state-of-the-art theories and technologies of Natural Language Processing,this thesis utilized data visualization tools to present collected report text in many aspects.Then,machine learning models were applied to classify diverse accident causes and comparsions on classification results were made to evaluate different models.Further,unstructed text descriptions of accidents were transferred into well-structed text with logic.TF-IDF was also applied to extract key words with much importance to accident text,which were deeply digged by the association rule algorithm to find hidden relations and risky factors' influences on safety accidents.The best CNN model on text classification of accident reports proposed by this thesis will promote its development in the construction industry and provide a relied tool and framework for risk analysis on safety management.The relations and rules among construction factors digged by the association rule algorithm will guide the construction production in later days and enhance humanization and specialization of safety management in the construction industry.