The Ministry of Employment and Labor releases its annual report on the present conditions of industrial disasters by aggregating and summarizing negligent accidents that occur at construction sites. Industry-specific accident and fatality rates, and disaster classification and statistics are aggregated in this report, but its effectiveness is low. This is due to the fact that it does not sufficiently present the direct causes of accidents or related information on their causal relation. However, this study utilizes a big-data method that has recently gained significant attention throughout all industrial and academic areas to collect Internet articles on fire-accidents that have occurred at construction sites over the last decade. In addition, principal component analysis was conducted to deduce season-specific factors according to time, location, inducer, and accident pattern. Based on this analysis, as for common factors, direct spark and oil mist were deduced. As work-related factors, negligent supervision and violations of the safety regulations were shown to cause fire-accidents, illustrating the man-made nature of such accidents. It was also found that secondary accidents such as collapses, burials, explosions, and suffocation have occurred when fires have broken out. The big-data analysis method utilized in this study is considered to be very effective and can be successfully utilized in the future for deducing high volumes of text data.

To combat the rutting effect and other distresses in asphalt concrete pavement, certain modifiers and additives have been developed to modify the asphalt mixture to improve its performance. Although few additives exist, nanomaterials have recently attracted significant attention from the pavement industry. Several experimental studies have shown that the use of nanomaterials to modify asphalt binder results in an improved oxidative aging property, increased resistance to the rutting effect, and improves the rheological properties of the asphalt mixture. However, despite the numerous benefits of using nanomaterials in asphalt binders and materials, there are various uncertainties regarding the environmental impacts of nano-modified asphalt mixtures (NMAM). Therefore, this study assessed a Nano-Silica-Modified Asphalt Mixtures in terms of materials production emissions through the Life Cycle Assessment methodology (LCA), and the results were compared to a conventional asphalt mixture to understand the impact contribution of nano-silica in asphalt mixtures. To be able to compare the relative significance of each impact category, the normalized score for each impact category was calculated using the impact scores and the normalization factors. The results showed that NMAM had a global warming potential of 7.44563 × 10³ kg CO₂-Eq per functional unit (FU) compared to 7.41900 × 10³ kg CO₂-Eq per functional unit of the conventional asphalt mixture. The application of LCA to NMAM has the potential to guide decision-makers on the selection of pavement modification additives to realize the benefits of using nanomaterials in pavements while avoiding potential environmental risks.

Construction projects are planned in a complex and dynamic environment characterized by high risks and uncertainties amidst resource constraints. Assessing construction schedule risk facilitates informed decision-making, especially in a resource-constrained situation, and allows proactive actions to be taken so that project objectives are not jeopardized. This study presents a stochastic multiskilled resource scheduling (SMSRS) model for resource-constrained project scheduling problems (RCSPSP) considering the impact of risk and uncertainty on activity durations. The SMSRS model was developed by integrating a schedule risk analysis (SRA) model (developed in MS Excel) with an existing multiskilled resource scheduling (MSRS) algorithm for the development of a feasible and realistic schedule. The computational experiment carried out on three case projects using the proposed SMSRS model revealed an average percentage deviation of 10.50%, indicating the inherent risk and uncertainty in activity durations of the project schedule. The core contribution of the proposed SMSRS model is that it: (1) presents project practitioners with a simple tool for assessing the risks and uncertainty associated with resource-constrained project schedules so that necessary response actions can be taken to ensure project success; (2) provides the small-scale construction businesses with an affordable tool for evaluating schedule risk and developing a feasible and realistic project schedule.

Road construction projects are complex endeavors, likewise the associated risks. Thus, identifying and assessing the potential risks associated with road projects is crucial to achieving project success. However, these associated risks are complex and dynamic, and the traditional risk assessment methods cannot accurately assess the risks. Hence, this paper presents a neural network approach by developing a deep neural network (DNN) model for assessing risk impact on project schedule and cost performance of road projects. The proposed DNN model was used to assess and predict the risk impact on 207 road projects in the Republic of Korea (ROK), and the accuracy was compared with a baseline neural network (BNN) and random forest regressor (RFR) models. The results show that the DNN model outperformed the BNN and RFR models, given the lowest mean squared error (MSE) and mean absolute error (MAE) values. The theoretical contribution of this study is related to risk assessment theory and the integration of expert judgment into a DNN model for effective risk assessment of road projects. Also, this study provides the practitioners with a systematic approach to predict accurate project schedule and cost performance considering the significant risk factors while enabling proper allocation of contingency allowance to schedule and cost baselines to guarantee project success.

Earned Value Management (EVM) has been established as a project management technique for project monitoring and control. The traditional EVM performs well in forecasting Cost Performance index and other cost metrics. However, in terms of schedule performance, the accuracy of the forecasted schedule metrics through the traditional EVM approach are always questionable. The schedule performance is not measured in time unit but rather in monetary units or uses cost information, which may cause misleading in the interpretation of the EVM schedule metrics. The schedule performance is not accurately forecasted, resulting in underestimating the estimate at completion (t). Even the renowned Earned Schedule also uses cost as a proxy to determine the earned schedule. This paper presents a new EVM tool, Duration estimate at completion (DEAC-model) developed to accurately forecast the time estimate at completion. DEAC-model uses the actual time spent on each activity, either in progress or upon completion, where the Performance is measured in time units. The benefits of DEAC-model to project management team and researchers are that it can be used: 1) to forecast schedule metrics accurately so that resources can be effectively allocated to complete the remaining activities, 2) as a gauge to assess if the project can be completed within the plan schedule, and 3) to apply time series and simple linear regression model concepts using excel worksheet syntax to forecast duration estimate at completion that is easily applicable.