As building information modeling (BIM) systems continue to be widely adopted, there is an increasing demand for an active construction schedule management system with more advanced decision-making capabilities. For example, if overlapping between construction activities is significant, the performance of construction operations for the corresponding activities may deteriorate. Thus, a viable construction schedule should be formulated in order to minimize overlapping of proximate construction activities. An active system can be regarded as a certain solution for this issue. The purpose of this study is to develop a systematic methodology and computer system for an optimal construction schedule simulation that minimize overlapping activities for the enhancement of a project’s operational performance. This study centers on identifying overlapping activities, applying fuzzy theory, and analyzing risk levels for schedule overlap issues. In addition, genetic algorithm (GA) theory is adopted for the mini...

Many construction firms have begun carrying out schedule management by using digital methods, such as 3D CAD and building information model (BIM)s. However, such methods are hampered by discrepancies between virtual models and real world models. This study develops an augmented reality (AR)-based 4D CAD system, which connects 4D and 5D objects with a real field image and an AR object. A method to link 4D objects and AR objects is proposed in order to implement several types of schedule information, as well as to enable the use of constantly changing schedule information through AR objects. Furthermore, a method for implementing AR objects based on connecting real-time field images is presented to help long distance managers become more accustomed to virtual objects and to enable better reflecting field environmental conditions in schedule management. In this study, the AR-based 4D CAD system using these methods is suggested and applied in a case study to verify the applicability of the system. The proposed system can be used for field monitoring for schedule management at job sites and to support cooperation and decision making between long distance managers and field engineers.

Currently, various guidelines regarding building information modelling (BIM) technology policy are being developed in different countries. However, for many companies, the cost-effectiveness of BIM investment remains unclear. Some studies suggest a return on investment (ROI) as the result of cost-effective analysis calculations, which can be obtained by the introduction of BIM. However, a lack of research has led to inconsistent metrics being applied to the calculation of BIM-ROI for various types of projects. The purpose of this study is to develop a system to evaluate the performance of BIM using a K-means clustering algorithm and ROI analysis to reflect the cost-effectiveness of BIM investment. The proposed system also includes methods for determining best-case projects with high similarities from existing case projects and benchmarking their evaluation know-how, and its usability was verified through experienced BIM users.

Mobile phones and drones are widely used to record images of construction sites in various stages of progress. Although site images can be used a lot during the construction period, they are mostly used only for recording simple construction results. If objects in a photographic image are recognized by a component of construction structure, the image could serve as a valuable tool in construction schedule management. In this study, authors present a framework for automatically recognizing objects for each component of a structure in a photographic image. To this end, after collecting and training a large amount of photographic images from the railroad bridge structures, an object detection method that can automatically recognize the construction components of the bridge structure based on deep learning model was introduced. Images are procured through web crawling, and the collected images are pre-treated for supervised training. The results of the deep learning model showed high performance in the pier and coping classes, and the slab showed a rather low accuracy, and it was confirmed that the degree of utilization of the detection results was significantly affected by the angle of shooting the image.