Drawing on institutional theory, this study examines how three types of isomorphic pressures (i.e., coercive, mimetic, and normative pressures) impact building information modeling (BIM) adoption activities in construction projects. The effects of these pressures are empirically tested with survey data collected from 92 construction projects in China. The results from partial least squares analyses indicate that coercive and mimetic pressures both significantly influence the extent of project-level BIM adoption. However, this study does not find support for a significant influence of normative pressures. The results further indicate that client/owner support plays a crucial but varied mediating role in the influences of these different isomorphic pressures. This study reinforces the need to regard project BIM adoption as a complexly socialized activity that is not only motivated by participants' rational needs to proactively address internal process problems, but also driven by external isomorphic pressures related to the acquisition of institutional legitimacy. The findings also provide insights into how different types of institutional forces can be better manipulated to facilitate the diffusion of BIM in the construction industry.Department of Building and Real Estat

Timely and overall knowledge of the states and resource allocation of diverse activities on construction sites is critical to resource leveling, progress tracking, and productivity analysis. Despite its importance, this task is still performed manually. Previous studies have taken a significant step forward in introducing computer vision technologies, although they have been oriented toward limited classes of objects or limited types of activities. Furthermore, they especially focus on single activity recognition, where an image contains only the execution of an activity by one or a few objects. This paper introduces a two-step method for recognizing diverse construction activities in still site images. It detects 22 classes of construction-related objects using convolutional neural networks. With objects detected, semantic relevance representing the likelihood of the cooperation or coexistence between two objects in a construction activity, spatial relevance representing the two-dimensional pixel proximity in the image coordinates, and activity patterns are defined to recognize 17 types of construction activities. The advantage of the proposed method is its potential to recognize diverse concurrent construction activities in a fully automatic way. Therefore, it is possible to save managers’ valuable time in manual data collection and concentrate their attention on solving problems that necessarily demand their expertise.