Critical Behavioral Risk Factors among Principal Participants in the Chinese Construction Industry

Predicting financial losses due to apartment construction accidents utilizing deep learning techniques

This study aims to generate a deep learning algorithm-based model for quantitative prediction of financial losses due to accidents occurring at apartment construction sites. Recently, the construction of apartment buildings is rapidly increasing to solve housing shortage caused by increasing urban density. However, high-rise and large-scale construction projects are increasing the frequency and severity of accidents occurring inside and outside of construction sites, leading to increases of financial losses. In particular, the increase in severe weather and the surge in abnormal weather events due to climate change are aggravating the risk of financial losses associated with accidents occurring at construction sites. Therefore, for sustainable and efficient management of construction projects, a loss prediction model that prevents and reduces the risk of financial loss is essential. This study collected and analyzed insurance claim payout data from a main insurance company in South Korea regarding accidents occurring inside and outside of construction sites. Deep learning algorithms were applied to develop predictive models reflecting scientific and recent technologies. Results and framework of this study provide critical guidance on financial loss management necessary for sustainable and efficacious construction project management. They can be used as a reference for various other construction project management studies.

Development of Model to Predict Natural Disaster-Induced Financial Losses for Construction Projects Using Deep Learning Techniques

This study goals to develop a model for predicting financial loss at construction sites using a deep learning algorithm to reduce and prevent the risk of financial loss at construction sites. Lately, as the construction of high-rise buildings and complex buildings increases and the scale of construction sites surges, the severity and frequency of accidents occurring at construction sites are swelling, and financial losses are also snowballing. Singularly, as natural disasters rise and construction projects in urban areas increase, the risk of financial loss for construction sites is mounting. Thus, a financial loss prediction model is desired to mitigate and manage the risk of such financial loss for maintainable and effective construction project management. This study reflects the financial loss incurred at the actual construction sites by collecting claim payout data from a major South Korean insurance company. A deep learning algorithm was presented in order to develop an objective and scientific prediction model. The results and framework of this study provide critical guidance on financial loss management necessary for sustainable and successful construction project management and can be used as a reference for various other construction project management studies.

Quantifying the Third-Party Loss in Building Construction Sites Utilizing Claims Payouts: A Case Study in South Korea

This study aims to quantify the losses to third-parties on construction sites by determining the loss indicators and identifying the relationship between the losses and the indicators to improve the sustainability on building construction sites. The growing size and intricacy of recent construction projects have resulted in the growth of losses, both in quantity and frequency. Notably, third-party losses are rapidly increasing owing to the urbanization of the environment and increases in construction scale. Therefore, for efficient and sustainable construction management, a financial loss assessment model is essential to mitigate and manage such loss. This study uses the third-party losses on construction sites obtained from a major South Korean insurance company to describe the difference from the material losses and to disclose the loss indicators based on actual economic losses. ANOVA analysis and multiple regression analysis are adopted to identify the variance and define the loss indicators and to make prediction models, respectively. Several groups of loss indicators are investigated, including construction information and the occurrence of natural disasters. The findings and results of this research afford an essential guide to sustainable construction management, and they can serve as a first stage loss assessment model for construction projects.

Sustainable Risk Assessment through the Analysis of Financial Losses from Third-Party Damage in Bridge Construction

Due to the recent introduction of innovative construction methods and technologies, construction projects increasingly require sustainability in their high degrees of specialization and complex work processes. This is due to a wide variety of new risk factors associated with construction projects that can lead to extensive and severe damage. When an accident occurs during a construction project, it can cause material, property, or bodily damage not only within the actual construction site but also outside, affecting third parties. This study analyzed the record of such third-party damage and the subsequent financial losses in bridge construction management, to identify the objective and quantified relationship of risk indicators related to the damage and losses. In order to assess the actual losses in construction projects, we adopted the loss claim payout data as recorded and provided by a major Korean insurance company, and conducted a multiple regression analysis to identify the loss indicators and to develop a loss estimation model. In this study, the analysis of the data indicated that the superstructure type, the foundation type, floods, and company ranking by the amount of the contract were the four statistically significant risk indicators that affected financial losses from third-party damage, among the nine variables used as independent variables, which included the superstructure type, foundation type, superstructure construction method, maximum span length, floods, typhoons, total construction cost, total construction period, and company ranking. As this study focused on identifying the risk factors and producing a loss assessment model quantified in numerical values, the results provide important references for assessing and minimizing the risks to third parties and the consequential financial losses in bridge construction, while promoting sustainability objectives.

The Social Risk of High-Speed Rail Projects in China: A Bayesian Network Analysis

In China, high-speed rail projects have brought huge social and economic benefits to the affected regions after they are completed. However, the potential externalities of such projects cause competition for the station during the project planning phase, thus triggering social risks. This paper studies the mechanisms responsible for generating the social risk associated with such high-speed rail projects. Employing typical case studies, a social risk list for a given project is established. Based on the risk list, a Bayesian network model is developed and verified through case studies, expert interviews, and expert grading. Using the model’s functions of reverse inference and sensitivity analysis, the key risk factors, sensitive risk factors, and maximum causal chain are identified. Countermeasures are then proposed to mitigate the social risk, such as increasing the transparency of and democratizing the planning process for high-speed rail projects, improving the mechanism by which local governments can express interest in such projects, and enhancing emergency management mechanisms. The findings provide points of reference for social risk management when it comes to planning high-speed rail projects and, more generally, offer significant guidance for socially sustainable decision-making processes for mega projects with massive externalities.

Approaches to Risk Identification in Public–Private Partnership Projects: Malaysian Private Partners’ Overview

The complexity of public–private partnership (PPP) projects ensures that risks could emerge and spread in unpredictable ways if they are not well identified and managed. The emergence of PPP projects has brought major changes in the construction industry, the most notable being in procurement methods influencing risk allocation to private parties. Thus, it is crucial to have an effective risk management for public and private partners to eliminate or minimise risks. Formulating an effective risk management system is a crucial challenge faced by both of parties in order to minimise or optimise risks. The aim of this study was to investigate the process of risk identification of private partners in Malaysian PPP projects. Data were collected throughout a 2-month period using a survey with a sample of nine Malaysian companies engaged in PPP projects, and the survey results were analysed using mean scores. The findings indicate that due to a lack of knowledge and experience of Malaysian private partners in the risk identification process, a comprehensive database for risk identification is highly necessary for the private sector. Another issue emerging from the findings is that it may be reasonable to use a combination of risk identification tools for PPP projects with a high level of complexity. The findings of the present study can greatly assist public and private partners to select the most appropriate tools for risk identification at the early stages of PPP projects.