Risk assessment, consisting of hazard identification and risk analysis, is an important process that can prevent costly incidents.
However, due to operational pressures and lack of construction experience, risk assessments are frequently poorly conducted. In order to
improve the quality of risk assessments in the construction industry, it is important to explore the use of artificial intelligence methods to
ensure that the process is efficient and at the same time thorough. This paper describes the adaptation process of a case-based reasoning
CBR approach for construction safety hazard identification. The CBR approach aims to utilize past knowledge in the form of past hazard
identification and incident cases to improve the efficiency and quality of new hazard identification. The overall approach and retrieval
mechanism are described in earlier papers. This paper is focused on the adaptation process for hazard identification. Using the proposed
CBR approach, for a new work scenario the input case, a most relevant hazard identification tree and a set of incident cases will be
retrieved to facilitate hazard identification. However, not all information contained in these cases are relevant. Thus, less relevant
information has to be pruned off and all the retrieved information has to be integrated into a hazard identification tree. The proposed
adaptation is conducted in three steps: 1 pruning of the retrieved hazard identification tree; 2 pruning of the incident cases; and 3
insertion of incident cases into the hazard identification tree. The adaptation process is based on the calculation of similarity scores of
indexes. A case study based on actual hazard identifications and incident cases is used to validate the feasibility of the proposed adaptation
techniques.
DOI