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.

The decommissioning of nuclear power plants (NPPs) is rapidly increasing because NPPs are not only no longer profitable in many cases but are also being decommissioned due to a lack of public acceptance or political reasons in many countries, particularly in Europe, following the explosion of the Fukushima Daiichi NPP. Accordingly, a significant body of research has focused on achieving safe, environmentally sound, and sustainable decommissioning in many countries where there is demand for NPP decommissioning. In order to achieve sustainable decommissioning that restores the NPP site to its pre-NPP environmental state, it is necessary to understand the safety, technology, and cost aspects as well as having the process and strategy to systematically promote them. Although there are a limited number of countries with experience and knowledge in the management of decommissioning multiple NPPs, researchers in countries just starting NPP decommissioning need diverse research information on how to formulate a sustainable decommissioning strategy as well as related factors. In particular, a systematic review of decommissioning strategies, such as DD, ID, and ET, and the influencing factors associated with each strategy is needed from the researcher’s point of view. In this regard, this study reviews the research literature on decommissioning strategies for nuclear power plants with a sustainable perspective. A systematic method involving a meta-analysis is used. The results of this study confirm that many researchers are most interested in DD and are dealing with ID and ET at the same level, but in reality, DD and ID are being adopted at similar rates. Thus far, only three ETs have been adopted in the United States. Most countries that have adopted ID are deemed to have been influenced by political decisions.

Recent studies on safety in various fields use the concept of safety climate to explain the causes of safety accidents. Many studies attempt to measure the safety climates and identify the causes for accidents in the high-risk construction industry. Studies have shown that the higher the level of the safety climate, the lower the accident rate at construction sites. Methods of measuring safety climate, including the NOSACQ-50 survey, have been presented. Studies on the methodology of measuring safety climate should be continued to improve reliability and precision. Although many studies have been conducted to measure safety climate, such as questionnaires, regression analysis, and suggestions for safety climate measurement methods, there are few studies on a systematic literature review of them. This requires a systematic literature review (SLR) of the studies conducted so far. This study conducted an SLR on the definition and measurement methods of safety climate in the construction industry published since 2000, when safety climate’s impact on accidents began to be established. This review study utilized the PRISMA method, analyzed 735 studies, and selected 57 papers finally. SLR was carried out for selected research works, and the results were summarized. There are three methods to measure safety climate: literature survey, questionnaire, and data analysis. Factor analysis, development of measuring model, development of questionnaire, statistical analysis, and machine learning were investigated as their sub-methods. This study’s results can be used as fundamental sources for improving existing methods and developing new methods of measuring safety climate in the construction industry.

Extensive use has been made of lifecycle-cost assessment to enhance the cost-effectiveness and resilience of facilities management. However, if such assessments are to be truly effective, supplemental information will be needed on the major costs to be expected over buildings’ entire lives. Electricity generation and distribution systems, for example, are absolutely indispensable to industry and human society, not least in the operation of buildings and other infrastructure as networks. The widespread disruption that ensues when such power systems are damaged often carries considerable repair costs. Natural disasters likewise can cause extensive societal, economic, and environmental damage. Such damage is often associated with lengthy power outages that, as well as being directly harmful, can hinder emergency response and recovery. Accordingly, the present study investigated the correlations of natural hazard indicators such as wind speed and rainfall, along with environmental data regarding the power failure in Florida caused by Hurricane Irma in 2017 utilizing multiple regression analysis. The environmental data in question, selected on the basis of a thorough literature review, was tree density. Our analysis indicated that the independent variables, maximum wind speed, total rainfall, and tree density, were all significantly correlated with the dependent variable, power failure. Among these, rainfall was the least significant. Despite there being only three independent variables in the model, its adjusted coefficient of determination (0.512) indicated its effectiveness as a predictor of the power outages caused by Hurricane Irma. As such, our results can serve the construction industry’s establishment of advanced safety guidelines and structural designs power transmission systems in regions at risk of hurricanes and typhoons. Additionally, insurance companies’ loss-assessment modeling for power-system facilities would benefit from incorporating the three identified risk indicators. Finally, our findings can serve as a useful reference to policymakers tasked with mitigating power outages’ effects on infrastructure in hurricane-prone areas. It is hoped that this work will be extended, facilitating infrastructure restoration planning and making societies and economies more sustainable.

So far, studies for predicting construction safety accidents have mostly been conducted by statistical analysis methods that assume linear models, such as regression and time series analysis. However, it is difficult for this statistical analysis method to reflect the nonlinear characteristics of construction safety accidents determined by complex influencing factors. In general, deep learning techniques are used to analyze the nonlinear characteristics of complex influencing factors. Therefore, the purpose of this study is to propose a framework for developing a deep learning model for predicting safety accidents for sustainable construction. For this study, 1766 cases of actual accidents were collected by the Korea Occupational Safety Authority (KOSHA) over the 10-year period from 2010 to 2019. Eight factors influencing accident prediction such as medical day, progress rate, and construction scale were selected. Subsequently, the predictive power between deep learning models and conventional multi-regression models was compared using actual accident data at construction sites. As a result, a deep neural network (DNN) improved predictive power by 9.3% in mean absolute error (MAE) and 10.6% in root mean square error (RMSE) compared to a conventional multi-regression model. The results of this study provide guidelines for the introduction of deep learning technology to construction safety management.

Reinforcing bars (rebar), which have the most embodied carbon dioxide (CO₂) per unit weight in built environments, generate a significant amount of cutting waste during the construction phase. Excessive cutting waste not only increases the construction cost but also contributes to a significant amount of CO₂ emissions. The objective of this paper is to propose a special-length-priority cutting waste minimization (CWM) algorithm for rebar, for sustainable construction. In the proposed algorithms, the minimization method by special and stock lengths was applied. The minimization by special length was performed first, and then the combination by stock length was performed for the remaining rebar. As a result of verifying the proposed algorithms through a case application, it was confirmed that the quantity of rebar was reduced by 6.04% compared with the actual quantity used. In the case building, a CO₂ emissions reduction of 406.6 ton-CO₂ and a cost savings of USD 119,306 were confirmed. When the results of this paper are applied in practice, they will be used as a tool for sustainable construction management as well as for construction cost reduction.