Abstract. Accurate prediction of the construction duration is imperative to the reliable cash flow analysis during the project planning phase when feasibility analysis is carried out. However, lack of information and frequent changes that occur as a result of a negotiation process between the owner and the designer in defining the project scope make it difficult to compute real-time construction duration. Domestic and foreign models for calculating the construction durations cannot be readily applied to computation of construction duration for general office buildings in Korea specifically during the project planning phase as there is a limit in its applicability due to numerous restrictions. Moreover, there are no preceding studies suggesting different computational approaches to predict the entire construction duration for office buildings with the approximate construction duration concept during planning phase. Therefore, based on the collected performance data, this study proposes a multiple linear regression model that facilitates reliable prediction of approximate construction duration for office buildings in the project planning phase. The model will allow the owner and other stakeholders to predict the real-time construction duration using the basic information on office buildings and to assess the construction durations incorporating frequent changes during the project planning phase.

PHC pile head cutting is an essential operation in foundation works, as it is needed to level the pile foundations. However, as it involves workers manually cutting the PHC pile with a grinder, the PHC pile head cutting process has several challenges with regard to safety, convenience, productivity, and quality. To address such problems, in this study, we define the core element technologies and automated work processes of an all-in-one attachment-based PHC pile head cutting robot that allow a series of operations to be performed in sequencerecognizing a cutoff line on PHC piles; cutting them; and separating, lifting, transporting, and unloading the severed top parts of the piles without the need to involve workers onsite. Additionally, a prototype of the robot is developed and subjected to performance evaluation and productivity analysis. The results of the performance evaluation and productivity analysis of the conventional and automated methods performed using Web-CYCLONE simulations indicate that the automated method can improve the productivity by 3.13% compared with the conventional method. It is anticipated that when deployed onsite, the proposed robot can not only increase the productivity but also improve the convenience and quality of work at the PHC pile head cutting job site.

The recently developed intelligent excavation robot in Korea is a fully automated excavator equipped with global 3D modeling capabilities for an entire earthwork site and an intelligent task planning system. The intelligent excavation robot includes features such as autonomous driving, 3D surround modeling, autonomous excavation, loading, etc. An intelligent excavation robot features technology that allows for accurate recognition of objects near the excavator, including the terrain of surrounding environments, location of obstacles in the excavator's path, and any approaching trucks and moving people. Such technology is critical to ensuring work quality and safety. In this study, we develop the hardware for a 3D surround laser sensing system that enables 3D image modeling of the terrain surrounding an intelligent excavation robot. By mounting a sensor onto an intelligent excavation robot, we conducted performance tests to determine the robot's 3D modeling capabilities of the terrains and obstacles at an actual earthwork site. The experimental results are applied to an object recognition system for detecting the properties of the terrain of the workspace around the excavator, any approaching people, trucks, obstacles, etc. The proposed hardware includes a wide range of applications in the development of future automated construction equipment.

Steel pipe pile head cutting work is performed to adjust the horizontal levels of piles, and it is essential for the stable transfer of an upper structure load to the ground. However, the field survey results show that steel pipe pile head cutting process is highly dangerous as laborers especially deal with gas and plasma cutting machines. Moreover, the laborers are exposed to continuous risks because the piles are frequently felled, lifted, moved, and loaded using construction equipment, such as excavators, immediately after the piles are cut. Recently, the authors of this study developed a prototype of a steel pipe pile head cutting robot and verified its performance through laboratory experiments to improve work safety, productivity, and the quality of steel pipe pile head cutting work. The purpose of this study is to secure the economic feasibility of robot development and verify the sustainable utilization of a developed robot by analyzing the comprehensive performance and economic efficiency throughout the life cycle of a steel pipe pile head cutting robot developed in South Korea. In this study, sensitivity analysis was also performed on the variables expected to have a significant influence or variables that must be considered for the future commercialization of the developed robot. When the developed robot is applied to construction sites in the future, its ripple effects will be significant because it will be possible to prevent labor safety accidents, improve work productivity, secure uniform quality, and reduce input costs.

Abstract:The amount and market size of apartment complex exterior wall painting work continues to increase each year in South Korea. Nevertheless, there are difficulties with the supply and demand of human resources due to the high risks associated with conventional painting work. To resolve these issues, research and development has recently been conducted on a Gondola-type Exterior Wall Painting robot (GEWPro). The aims of this study were to develop a performance evaluation and life cycle cost (LCC) analysis model for a GEWPro and deduce its performance and economic efficiency through a case study. According to the results, the performance of the automated method was 16.8% higher than that of the conventional method, and the economic efficiency was also superior (benefit/cost ratio 6.39). These results show that the proposed performance evaluation and LCC analysis model can predict the productivity and economic efficiency of automated methods.