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This paper presents an embodied GHG assessment of a completed highway bridge renewal project in Canada and offers new insights on the strengths and limitations of BIM-based GHG assessments. Bridges play a critical role in transportation infrastructure. As reducing environmental impacts has taken on increasing urgency across the construction sector, bridges have received relatively little study. A quantitative understanding of the embodied greenhouse gas emissions (GHG) in bridges is needed to inform future infrastructure planning, design, and construction. Recent efforts to streamline environmental assessment in construction have led to the creation and adoption of Building Information Model (BIM)-based embodied GHG assessments, taking advantage of the quantity take-off functionality of BIM. However, as BIMs are rarely developed with environmental assessment as the primary goal, their effectiveness for GHG accounting can be limited. GHG assessments of the case study bridge using a BIM prepared before construction and using material and energy quantities records collected on-site during construction, are compared. Using only the quantities from preconstruction BIM, the embodied GHG for the case study bridge is 1.3 × 106 kgCO 2 e. After the adjustments for on-site data collection and factors commonly excluded from BIM, such as on-site fuel use from machinery, it rises to 4.06 × 106 kgCO 2 e, an increase of 212%. Results illustrate the need to change the scope and detail of BIMs if they are to be effective for embodied GHG assessment, and the potential limitations of using BIM in embodied GHG assessment. • Embodied GHG of a bridge is assessed using onsite records and preconstruction BIM. • Assessment based on onsite records resulted in a 212% increase in embodied GHG. • New insights on strengths and limitations of BIM-based GHG assessment are presented. • Results demonstrate BIM-based embodied GHG assessments need to be applied with care. • Scope and detail of BIMs need to be changed for effective embodied GHG assessment. [ABSTRACT FROM AUTHOR]

Accurate data collected from on-going construction projects assists project field engineers with tracking the progress of construction work. The comparison of as-planned schedules against the as-built status of construction enables the involved parties to determine project performance. Periodic monitoring of project performance enables timely identification of discrepancies between the schedule baseline and the project schedule. The rapid identification of discrepancies allows necessary measures to be taken to minimize the impact of a delay on the construction workflow. In transportation projects, the traditional means of acquiring as-built data from a construction site prevents involved parties from receiving the required information from the site in a timely manner. The delay in the communication of information ultimately causes subsequent delays in implementing necessary courses of action targeted toward improving the workflow. Therefore, this project develops a technology-supplemented progress monitoring approach. The approach implements as-built data with a high level of detail is obtained from a jobsite within a reasonable period of time using lidar technology while ensuring the safety of the data collector. The proposed framework utilizes point cloud data obtained using mobile lidar technology and 4D design models to identify deviations of the performed work from the planned work. The framework was tested using as-built data acquired from an on-going bridge construction project, and Percentage of Completion (POC) for the as-built bridge elements are calculated and compared with the as-planned POC. The differences between these two POC values for every element, on a particular scan date, are used for assessing the performance of the proposed framework. The obtained POC difference values ranged from −7% to 6% for most elements, which shows that the developed framework enables tracking the completion of individual bridge elements in an accurate and efficient manner. • 3D design models, construction schedule and mobile lidar data were used to report project progress information. • POC for the as-built bridge elements are calculated and compared with the as-planned POC. • Individual bridge elements can be monitored accurately and efficiently using the proposed framework. • The manual processing time for each epoch is approximately 1 h and 15 min for the case study. [ABSTRACT FROM AUTHOR]