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Installation time for precast concrete (PC) components varies due to component-, location-, and weather-related factors. Because such variability affects the cost, time, and sustainability performance of PC projects, it is important to identify and classify the factors influencing installation time for different types of PC components. Against this background, this study aims to empirically investigate variability in installation time for PC components and different work steps and then identify the factors that affect particular work steps most significantly. To achieve the goal, this study gathered installation time and other influencing factors data from an actual project for 25 days and analyzed the data using a descriptive approach and the recursive feature elimination with cross-validation (RFECV) analysis. The analysis result provides empirical evidence that different PC types and work steps are involved with different levels of variability in installation time. The highest level of variability was shown in PC beams and assembly work. Regarding the factors influencing installation time, it is shown that different work steps are affected by different sets of influencing factors, with temperature and wind speed as the most common influencing factors across different PC types and work steps. This study contributes to the body of knowledge of off-site construction management by offering empirical evidence on the variability in PC installation time and the specific factors affecting PC installation time. [ABSTRACT FROM AUTHOR]

Inspecting and assessing existing utility poles has become increasingly important for reducing the vulnerability of power distribution infrastructure systems in disaster situations, which can enhance community resilience. Although vision-based systems have been applied to detect faults in power distribution infrastructures, little research currently exists on assessing component- and network-level failures of utility poles based on their geometric and environmental information. This paper aims to propose a new data-driven approach to support risk-informed decision-making for utility maintenance under extreme wind conditions. Large-scale open-source imagery from Google Street View is used to assess geometric properties of utility poles (i.e., leaning angle). Then the failure probability of utility poles is analyzed under varying conditions (e.g., age, leaning angle, and wind loads) in a three-dimensional virtual city model. The proposed method is tested through case studies in Texas to (1) validate an algorithm for estimating leaning angles of utility poles and (2) understand the progress of failures of leaning utility poles from a network perspective. The outcomes of the case studies demonstrate that the proposed method has the potential to leverage large-scale open-source visual data to assess the vulnerability of utility pole networks that may lead to cascading failures in power distribution infrastructure systems. Based on the proposed virtual environment, the method is expected to enable practitioners to facilitate risk-informed decision-making against disaster situations, which creates an opportunity for prioritizing maintenance tasks regarding power distribution infrastructures. [ABSTRACT FROM AUTHOR]