Building information modelling (BIM) has been adopted in the construction industry. The success of BIM implementation relies on the accurate building information stored in BIM models. However, building information in BIM models can be inaccurate, out-of-date, or missing in real-world projects. 3D laser scanning has been leveraged to capture the accurate as-is conditions of buildings and create as-is BIM models of buildings; this is known as the scan-to-BIM process. Although industry practitioners and researchers have implemented and studied the scan-to-BIM process, there is no framework that systematically defines and discusses the key steps and considerations in the process. This study proposes an application-oriented framework for scan-to-BIM, which describes the four major steps of a scan-to-BIM process and their relationships. The framework is oriented towards the specific BIM application to be implemented using the created as-is BIM, and includes four steps: (1) identification of information requirements, (2) determination of required scan data quality, (3) scan data acquisition, and (4) as-is BIM reconstruction. Two illustrative examples are provided to demonstrate the feasibility of the proposed scan-to-BIM framework. Furthermore, future research directions within the scan-to-BIM framework are suggested.

In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset containing complex environmental noise is produced. Different shooting distances, angles, and lighting conditions are considered. Validated through the same dataset and compared with You Only Look at CoefficienTs ++ (YOLACT++) and DeepLabv3+, the proposed method shows higher accuracy for crack segmentation under complex backgrounds. Specifically, the crack damage detection based on the YOLOv5 method achieves a mean average precision of 91%; the modified Res-UNet achieves 87% intersection over union (IoU) when segmenting crack pixels, 6.7% higher than the original Res-UNet; and the developed crack surface feature algorithm has an accuracy of 95% in identifying the crack length and a root mean square error of 2.1 pixels in identifying the crack width, with the accuracy being 3% higher in length measurement than that of the traditional method.

Upon the completion of construction projects, quality assurance/quality control (QA/QC) must be conducted to guarantee the project quality complies with certain standards. The conventional QA/QC approach relies on manual measurements using conventional tools, which is inefficient and costly. Recently, terrestrial laser scanning (TLS) has been adopted for construction QA/QC due to its high speed and accuracy. However, research is still lacking to quantitatively analyze the potential time and cost benefits of TLS-based geometric QA/QC, and it is still not clear how and when a construction company should deploy TLS-based QA/QC. Hence, this study aims to provide a quantitative analysis of the time and cost benefits of TLS-based geometric QA/QC of buildings in comparison with the conventional QA/QC approach. First, a QA/QC checklist is determined based on relevant codes, and the processes and tasks of both conventional and TLS-based QA/QC approaches are identified. Second, a case study is conducted to collect the QA/QC time and cost for both approaches, and analyze the time and cost on both project and company levels. Third, the analysis results are further discussed considering potential technology advancements, fluctuations of cost items, changes of checklist items, and training needs. The results demonstrate that the TLS-based QA/QC approach is more efficient than the conventional QA/QC approach due to reduced data collection time. With regard to the cost, the TLS-based QA/QC approach with rented TLS is the most economical choice for a single project, and the TLS-based QA/QC approach with purchased TLS is the most economical choice for a company in the long term.

Given that traditional façade visual inspection entails laborious, dangerous, and inefficient manual work, automation-enabled façade visual inspection has become a prevailing trend in both academia and industry. However, automation-enabled applications often encounter uncertainty problems. For automation-enabled façade visual inspection, uncertainty in reliability and efficiency is an important factor that determines the value of introducing automation to façade visual inspection. During automation-enabled façade visual inspection, human efforts play important roles throughout the whole process and compose a human–cyber–physical system. Therefore, human-related activities and human factors are prominent causes of uncertainty in automation-enabled façade visual inspection. To understand human-related uncertainty, this work designed a Delphi study with an expert panel to quantitatively evaluate human-related activities and human factors. Also, an optimized fuzzy Delphi method was adopted to process the collected evaluation opinions. Based on the results, the most critical activities and human factors influencing uncertainty were extracted. Additionally, a structure of uncertainty generation was developed to analyze the evaluation results and provide recommendations for uncertainty control. This research contributes to facilitating understanding of the uncertainty problem in human–cyber–physical systems and to providing effective recommendations for uncertainty control in automation-enabled façade visual inspection.