Earthwork scheduling (during the planning phase of road construction) is an important task that directly affects the cost and time of a project. However, the current scheduling methods are not performed at a detailed level and carry forward gaps from the actual schedule in the construction stage, causing problems, including time delays and additional costs, during the construction stage and thereby leading to the modification and supplementation of existing plans. Many studies related to scheduling have focused on process optimization or automation; therefore, the gaps between the planning and construction stages have not been studied well. These gaps must be determined in advance to solve the fundamental problem of earthwork scheduling in road construction. Therefore, this study proposes a detailed activity-based scheduling model for earthwork at the planning stage to minimize the gap between the planned schedule and the actual process at the construction stage. The proposed model comprises a detailed activity-based database of earthwork in road construction (DADER), which enables the subdivision of the existing earthwork schedule plan, and a dynamic programming (DP) procedure, which enables the combination of activities. The earthwork schedule at the case site, planned for only three activities, is subdivided using DADER into 36 activities. Additionally, the DP procedure is used to derive a combination of activity alternatives that minimizes the cost among the conditions that satisfied the input target duration. The model is verified through an expert survey using a 7-point Likert scale. Results show that the model has cost efficiency (4.19), onsite applicability (4.70), and task efficiency (4.48). The findings indicate that performing scheduling during the planning stage of earthwork projects can help reduce additional work caused by the gap between the site and the plan. This study will help improve the productivity of road construction projects by providing correct process optimization and automation research data.

Recently, the application of 3D printing in the production of curved panels has increased due to the irregular shape of free-form buildings. In general, 3D printing based on additive manufacturing (AM) methods requires various supports that cause a waste of printing materials and an increase in production time. In this study, we proposed a method for printing a pair of panels that can hold each other through the minimal support connected between each panel. However, this printing method causes an additional non-productivity factor called the non-printing path for the nozzle to move between the pair of panels. Therefore, we also developed an optimal layout model that can minimize non-printing paths and used the genetic algorithm (GA) for its calculation. As a result of applying the optimization model proposed in this study through the case study, the non-printing path was reduced by 18.54% compared with that from the existing method, and the non-printing time was reduced by 34.41 h. The total production time, including non-printing time and printing time, was reduced by 3.89%, and the productivity was improved by 4.04%. The model proposed in this study is expected to minimize unproductive factors that occur in the process of manufacturing curved panels and reduce the energy consumption.

With the advent of the Fourth Industrial Revolution, construction technology innovation through high-tech convergence is actively taking place, and the smart construction technology market is growing rapidly. However, as it focuses on the use of individual technologies, research into the factors that have a major impact on their intended effect is insufficient. Thus, this study investigates these factors and their mutual influences from various perspectives to promote the use of smart technology to improve construction projects. Ten performance-influencing factors were derived from four perspectives based on the balanced scorecard technique. In addition, based on a survey of projects that use smart technology, the current status of its application and performance characteristics were analyzed, and a causal relationship model among the factors was presented. This study provides a foundation for identifying major areas for the efficient use of smart technology and performance measurement, and it will contribute to the introduction and activation of smart construction technology.