Integrating Information Entropy and Latent Dirichlet Allocation Models for Analysis of Safety Accidents in the Construction Industry

Abstract

Construction accident investigation reports contain critical information, but extracting useful insights from the voluminous Chinese text is challenging. Traditional methods rely on expert judgment, which leads to time-consuming and potentially inaccurate results. To overcome this problem, we propose a novel approach that combines text mining techniques and latent Dirichlet allocation (LDA) models to analyze standardized accident investigation reports in the Chinese construction industry. The proposed method integrates an information entropy term frequency-inverse document frequency (TF-IDF) weighting scheme to evaluate term importance and accounts for word and model uncertainty. The method was applied to a set of construction industry accident reports to identify the key factors leading to safety accidents. The results show that the causal factors of accidents in Chinese accident investigation reports consist of keywords and negative expressions, including “failure to timely identify safety hazards” and “inadequate site safety management”. Failure to timely identify safety hazards is the most common factor in accident investigation reports, and the negative expressions commonly used in the reports include “not timely” and “not in place”. The information entropy TF-IDF method is superior to traditional methods in terms of accuracy and efficiency, and the LDA model that considers word frequency and feature weights is better able to capture the underlying themes in the Chinese corpus. And the subject terms that make up the themes contain more information about the causes of accidents. This approach helps site managers more quickly and effectively understand the causal factors and key messages that lead to accidents from incident reports. It gives site managers insight into common patterns and themes associated with safety incidents, such as unsafe practices, hazardous work environments, and non-compliance with safety regulations. This enables them to make informed decisions to improve safety management practices.

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

“…In construction process field, 20 out of 61 observations were made regarding the construction of buildings, dams, roads, and tunnels. Within this field, the 60 out of 202 observations covered topics such as construction delays [22], crane, drilling and excavation tasks [14,18,24,39,48,70,74]; geological conditions [54], scaffolding collapse [68]; transport delays [31]; tunnelling [28,36,37,41,43,55,67]; workers and machinery location [34,40]. According to Erzaij et al [22], project suspensions are among the most persistent tasks facing the construction sector, due to the difficulty of the industry and the essential interdependence between the bases of delay risk.…”

“…In Occupational accident field, 16 out of 61 papers dealt with data on accidents and injuries at work from 2014 to 2023. In the class "occupational accidents," the 89 out of 202 submissions covered the following topics: reporting of accidents [3,26,27,29,32,35,43,45,49,[59][60][61][62]75,76] and days away from work. On this topic, Yelda et al analysed textual narratives to predict injury outcome and days off work in a mining operation.…”

“…associated with the collapse of scaffolding [41] and slope instability [10,17]. Liu et al analysed data from sophisticated and technologically innovative machine monitoring, capable of returning and processing geological data, faults and predicting the progress of TBM and maintenance, avoiding downtime and inspections, were analysed [43]. According to Schindler et al [55] and Leng et al [36], the use of satellite data has proved to be a winning strategy compared to ground surveys.…”

“…Liu et al underline the significance of employing innovative and efficient safety management technologies, along with new management approaches and automated methods based on artificial intelligence, to detect and eliminate risks promptly. According to the authors, these innovative technologies would mitigate any deficiencies in site management, significantly improve site safety management and eliminate risks at source [43]. An increasingly widespread orientation towards an automated management of the site or parts of it, would not only lead to an improvement in the health and safety of the processes but also a significant improvement in the quality of the data coming from the construction field.…”

Harnessing Generative Pre-Trained Transformers for Construction Accident Prediction with Saliency Visualization

“…The construction sector is also witnessing a data surge and an increasing emphasis on leveraging written text, particularly within the domain of construction safety through digital accident reports [6,7]. The ability to learn from past accidents, incidents, and near misses holds paramount importance in preventing future injuries [6,[8][9][10].…”