A half-precast concrete slab system (HPCSS) is reported to exhibit excellent structural performance when compared with traditional slab systems. However, there is a lack of extant research examining the construction issues of an HPCSS. Thus, in this study, we analyze the construction process and productivity of applying an HPCSS by using a simulation method with the data collected from an actual construction case. The results indicate that (i) the construction productivity of HPCSS is 1.7 times that of a traditional slab system, (ii) the cost per productivity unit of HPCSS exceeds that of a traditional slab system, and (iii) critical resources affecting the HPCSS productivity include form crew and rebar crew. The results of this study suggest that it is possible to develop an optimal construction plan of a construction site in which an HPCSS is installed, and that the HPCSS can be actively applied in the future.

Over the past decades, automation and robotics have been widely adopted in the construction industry as a promising solution for troubleshooting issues. This field is highly interdisciplinary and rapidly changing, and new concepts and ideas are being actively introduced in association with the existing ones. To reveal new insights, the evolution of knowledge in this field needs to be characterized by identifying important knowledge elements and research trends from published data. This study investigated research trends and network characteristics in the field of construction automation and robotics by using a keyword network analysis based on published articles. We analyzed the structure of the keyword network and identified major research topics and their relationships. Notable findings of the research characteristics are as follows. 1) Popular research themes with a set of closely connected keywords continue to be selected and contribute to the evolution of the network. 2) As an interdisciplinary research area, popular keywords that actively interact with other keywords also bridge different clusters of related research concepts. This study could provide useful insights and a better understanding of the knowledge structure and research trends as well as information on future directions in the construction automation and robotics field.

Recently, the construction industry has experienced an increase in the volume of renovation work aimed at improving the physical and economic performance of buildings. However, owners often hesitate to execute renovation projects because they find it difficult to guarantee an improvement in the monetary value of buildings after their renovation. Therefore, by applying multiple regression analysis to data collected from 98 actual renovated office cases, this study develops a model to enable convenient estimation of the monetary value of renovated office buildings compared with existing methods. The application of the model to 20 validation cases shows: (1) an average error rate of 10.05%; and (2) the need for comparatively less information for implementing the model. Essentially, it will enable the owners to make decisions regarding the execution of building renovation projects based on an estimation of the monetary value of buildings after renovation, which will bolster the renovation market.

The objective of this study is to evaluate the feasibility of deep-learning-based segmentation of the area covered by fresh and young concrete in the images of construction sites. The RGB images of construction sites under various actual situations were used as an input into several types of convolutional neural network (CNN)–based segmentation models, which were trained using training image sets. Various ranges of threshold values were applied for the classification, and their accuracy and recall capacity were quantified. The trained models could segment the concrete area overall although they were not able to judge the difference between concrete of different ages as professionals can. By increasing the threshold values for the softmax classifier, the cases of incorrect prediction as concrete became almost zero, while some areas of concrete became segmented as not concrete.