Work Accidents Correlation Analysis for Construction Projects in Northern Greece 2003–2007: A Retrospective Study

A deep neural network algorithm-based approach for predicting recovery period of accidents according to construction scale

Despite ongoing safety efforts, construction sites experience a concerningly high accident rate. Notwithstanding that policies and research to reduce the risk of accidents in the construction industry have been active for a long time, the accident rate in the construction industry is considerably higher than in other industries. This trend may likely be further exacerbated by the rapid growth of large-scale construction projects driven by urban population expansion. Consequently, accurately predicting recovery periods of accidents at construction sites in advance and proactively investing in measures to mitigate them is critical for efficiently managing construction projects. Therefore, the purpose of this study is to propose a framework for developing accident prediction models based on the Deep Neural Network (DNN) algorithm according to the scale of the construction site. This study suggests DNN models and applies the DNN for each construction site scale to predict accident recovery periods. The model performance and accuracy were evaluated using mean absolute error (MAE) and root-mean-square error (RMSE) and compared with the widely used multiple regression analysis model. As a result of model comparison, the DNN models showed a lower prediction error rate than the regression analysis models for both small-to-medium and large construction sites. The findings and framework of this study can be applied as the opening stage of accident risk assessment using deep learning techniques, and the introduction of deep learning technology to safety management according to the scale of the construction site is provided as a guideline.

Key Factors in Crane-Related Occupational Accidents in the Spanish Construction Industry (2012-2021)

The construction industry is one of the riskiest sectors worldwide, with crane operations being one of the most dangerous activities. The aim of this study was to gain insight into the key factors involved in crane-related occupational accidents in the construction industry in Spain. To this end, 1314 accidents involving cranes were analyzed from a total of 241,937 accidents that occurred in the construction of buildings. The data were collected from the Spanish government's occupational accident statistics corresponding to the years 2012-2021. The results evidenced a statistically significant relationship between cranes as the material agent and the size of the company, with 95% of cases corresponding to small- or medium-sized companies (less than 250 employees). Additionally, it shows how the crane operator is identified as a material contributor to crane accidents in the construction industry, and may be considered a key component to these accidents. In conclusion, improving the knowledge gained about the key factors in crane-related accidents at work in the construction industry provides essential information that helps to design and implement appropriate preventive measures to avoid the recurrence of unwanted events with these machines.

Determining the Reliability of Critical Controls in Construction Projects

Across the global construction industry, fatalities continue to occur from high-risk activities, where the risk controls have been defined; however, these were unreliable. In the mining industry, Critical Control Risk Management has provided positive results in reducing major accidents, which raises the question, could the Critical Control approach reduce the fatality rate in the construction industry? This study analyzed 10 years of serious and fatal incident investigation reports from four international construction companies to (i) assess the reliability of their Critical Controls (CCs) and (ii) assess the factors that affect the reliability of CCs. The results show the reliability of CCs, measured by implementation and effectiveness, averaged just 42%. Insight into human performance and organizational factors, including risk identification, decision-making and competency, together with supervision, job planning and communication, were identified as opportunities to improve the reliability of CCs. The study used bowtie diagrams with real event data to find the actual CC reliability. This appears to be the first published study that reports on the reliability of critical risk controls in construction. It demonstrates a feasible method for determining and communicating control effectiveness that can be used to deliver meaningful insights to industry practitioners on actual control performance and focus areas for improvement. In addition, actionable findings directly related to individual CCs can be derived that enable the participating organization to focus resources on improving specific verification processes. The results confirm the applicability of CCs for the Major Accident Event hazards analyzed and highlights that further reviews are required on the factors that need to be considered when implementing a CC program. This paper details our methodology and results, to assist others applying CCs as a risk management tool.

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.

Accident factors per construction type and stage: a synthesis of scientific research and professional experience

Following manufacturing, construction is the industry that is responsible for the majority of work-related accidents due to the complexity and uniqueness of each construction site. The accidents that occur on construction sites reflect the safety culture and the general health and safety (H&S) policy in the construction field. The purpose of this paper is to investigate the frequency of accidents and their contributing factors in each stage of 9 different types of construction. This paper utilizes data from published scientific literature to extract the universal contributing factors to be evaluated in the Greek construction industry and to compare results with documented international accidents. The riskiest construction stages per construction type and the main factors behind them are determined. The results show contradictions regarding the contributing factors as perceived by Greek professionals and those verified by international real accident data.

Factors for the Automation of the Creation of Virtual Reality Experiences to Raise Awareness of Occupational Hazards on Construction Sites

Two of the differential characteristics of the AECO sector (architecture, engineering, construction and operation) are barriers for the mass creation of training materials for its workers. On the one hand, the workplace is unique and changing over time; on the other, the aging trend of its workers and the unattractive nature of the industry for new generations of professionals. These two problems can be tackled by virtual reality technologies, which allow the agile creation of all kinds of scenarios, while their current technology may be attractive to young people and intuitive for everyone. This work shows the results of an investigation that seeks to provide automated tools based on virtual reality experiences to support learning in occupational risk prevention. This objective is part of the development of a culture for prevention, which allows the treatment of the human factor, with all its complexity and casuistry. The proposal includes the development of a process and tools that allow replicating the specific scenario where the work will be carried out, incorporating risks and probable incidents, systematically establishing cause-effect relationships, incorporating a narrative (storytelling) that provides emotional meaning to users and Lastly, the creation of a workflow that facilitates the agile development of these virtual reality experiences for each specific work.

4D-BIM-Based Workspace Planning for Temporary Safety Facilities in Construction SMEs

Temporary safety facilities (TSFs) are an essential support system providing necessary protection to workers during construction activities, which are targeted towards preventing the occurrence of incidents and accidents at the construction site; however, the schedule and location of installation and demolition of TSFs continue to rely on labor experience, and are often omitted from formal drawings or documents. This results in thousands of accidents in the construction industry, especially in construction small and medium-sized enterprises (SMEs) because of their several limiting factors; therefore, this study proposes automatic workspace planning for TSFs based on construction activities, which is a systematized approach for construction SMEs to practice occupational health and safety (OHS). By using building information modeling (BIM) and add-in algorithm, safety facilities can be simulated and visualized to integrate into the designated workspace. The developed system was implemented utilizing 4D-BIM for TSFs installation and validated with a case study on a residential building project. The result revealed that the visualized TSF produces a better understanding of safety measures with regard to project schedule. Additionally, TSFs workspace planning provides an affordable approach that motivates safety practices among the SMEs; consequently, the effectiveness of construction safety measures and their management is enhanced appreciably.