The special collection on Future of Smart Construction and Infrastructure is available in the ASCE Library ( technological and scientific revolution presents unprecedented opportunities to reshape the way our infrastructure is built and managed. Wireless and mobile sensing technologies, including mixed reality capture technologies (e.g., lidar, RGB-D, and vision), wireless infrastructure sensors, wearable sensors, robotics, and unmanned aerial systems (UASs), have generated large amounts of high-resolution data on infrastructure construction and management. In addition, the recent advance of artificial intelligence (AI) and big data analytics enables exploiting such data, making data-driven decisions, and improving the efficiency of operations.This special collection aims to seek contributions in data-driven approaches to help inform the future of smart construction and infrastructure. In particular, this collection invited extended papers presented at the 2019 ASCE International Conference on Computing in Civil Engineering (i3CE), held in Atlanta, June 17–19, 2019. The special collection discusses emerging areas of computing in civil engineering, including smart and connected health and communities (Kim et al. 2020), construction field robotics (Jeong et al. 2021), vision-based construction activity analysis (Calderon et al. 2020), wearable stress monitoring biosensor systems (Lee et al. 2020), infrastructure deterioration condition prediction (Liu and El-Gohary 2020), and restoration performance assessment (Chowdhury et al. 2020). Short-listed papers among many excellent papers presented at the i3CE 2019 conference were invited for extended paper publications in this collection. This special collection supports a broader discussion regarding the future of smart construction and infrastructure enabled by advanced sensing technologies and data analytics.References Chowdhury, S., J. Zhu, and W. Zhang. 2020. “Optimized restoration planning of infrastructure system-of-systems using heterogeneous network flow simulation.” J. Comput. Civ. Eng. 34 (5): 04020032. Lee, G., B. Choi, H. Jebelli, C. R. Ahn, and S. Lee. 2020. “Noise reference signal–based denoising method for EDA collected by multimodal biosensor wearable in the field.” J. Comput. Civ. Eng. 34 (6): 04020044. Liu, K., and N. El-Gohary. 2020. “Fusing data extracted from bridge inspection reports for enhanced data-driven bridge deterioration prediction: A hybrid data fusion method.” J. Comput. Civ. Eng. 34 (6): 04020047.

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