Cracks are an important symptom of pavement deterioration and deficiency. Accurate and complete information regarding pavement cracks is critical to determining pavement maintenance schedules, methods, and budgets. Two-dimensional (2D) pavement images are used in practice for crack detection and segmentation. Automatic crack detection and segmentation based on 2D images are challenging because of (1) low contrast between cracks and surrounding pavement; (2) complicated patterns of cracks; and (3) intensity inhomogeneity along cracks. To address these challenges, this paper presents a novel method to automatically detect and segment pavement cracks from 2D images. Specifically, the proposed method starts with the use of a steerable matched filter to generate a crack saliency map, which enhances the contrast between cracks and surrounding pavement and captures crack discontinuity and curvature. Analysis of the crack saliency map leads to a coarse crack region and rough estimates of crack properties. The coarse crack region is then fed into a region-based active contour model, and a level set evolution method is employed to implement the model for crack segmentation. The estimated crack properties provide information to automatically adjust the parameters of the active contour model for effective and efficient crack segmentation. The proposed method was tested using 65 pavement images with various cracks. The proposed method achieved average precision of 92.6%, recall of 85.1%, and F-measure of 88.7%.

A pothole is a severe pavement distress that can compromise pavement rideability and safety and can be the cause of expensive damage claims. The detection and evaluation of potholes are predominantly manual and time-consuming. Although sensing technologies such as global positioning systems (GPS), stereovision systems, and ground penetrating radar (GPR) now can be combined to collect pavement condition data for assessment, the raw data returned by these sensors are often processed individually and separately. This isolated approach to data processing hinders the potential efficiency and effectiveness of multisensor systems. This paper proposes a method to integrate the processing of two-dimensional images and GPR data to automate accurate and efficient pothole detection. First, the images and GPR scans are preprocessed to filter out noise and enhance the essential clues related to potholes. Second, a novel pothole detector was designed by investigating the patterns of GPR signals reflected by poth...

Underground utility incidents, such as utility conflicts and utility strikes, result in time and cost overruns in construction projects, property damages, environmental pollution, personnel injuries, and fatalities. A main cause of recurrent utility incidents is the noncompliance with the spatial configurations between utilities and their surroundings. Utility specifications usually contain textual descriptions of the spatial configurations. However, detection of spatial defects, according to the textual descriptions, is difficult and time consuming. This deficiency is because of the lack of spatial cognition in many rule-checking systems to process massive amounts of data. This study aims to automate utility compliance checking by integrating natural language processing (NLP) and spatial reasoning. NLP algorithm translates the textual descriptions of spatial configurations into computer-processable spatial rules. Spatial reasoning executes the extracted spatial rules following a logical order in ...

Enhancing workplace safety continues to be a major task in the construction industry. Approximately 75% of struck-by fatalities are caused by inappropriate spatial-temporal relationships between construction workers and heavy equipment. Construction safety can be improved if the location and movement of heavy equipment are tracked in real time. However, detecting and tracking heavy equipment with kinematic joints and changing poses, such as excavators, is still a challenge for vision-based sensing methods. This study proposes to detect and track excavators using stereo cameras based on hybrid kinematic shape and key node features. Specifically, templates of excavator components are synthesized for detection following kinematic constraints of each component. Thereafter, a fast directional chamfer matching algorithm is used to detect the excavator components, and the detected components are articulated at the key nodes. Finally, the three-dimensional positions of the key nodes are tracked through tr...

The coronavirus disease 2019 (COVID-19) pandemic has brought unprecedented impacts (e.g., labor shortage, suspension and cancellation of projects, and disrupted supply and logistics) on the US construction industry. To address challenges caused by the pandemic, it is critical for the construction industry to develop a clear understanding of how the pandemic has affected the industry and how it will change in the future. However, assessing the impacts of COVID-19 on the construction industry is challenging due to the broad influence of the pandemic and the dynamic nature of the industry. The Purdue Index for Construction (Pi-C), which was developed as an indicator based on five dimensions and corresponding metrics to measure the health status of the construction industry, offers an opportunity to understand the impact of the pandemic. In this context, this paper presents a study to reveal the relationship between COVID-19 and the health status of the industry as measured through Pi-C and predict the future trend of the construction industry. This study achieves the objective via the three steps. First, the relationship between the pandemic and Pi-C metrics is identified using the Granger causality test and structural equation modeling (SEM) analysis. Second, multivariable prediction models are developed based on a long short-term memory (LSTM) network—a deep learning algorithm—to predict Pi-C metrics in the future. Third, forecasted Pi-C metrics are integrated into the existing Pi-C structure to analyze the impacts of the COVID-19 pandemic and predict its trends in 2021–2022. The results revealed that the impacts of the pandemic were conspicuous in two Pi-C dimensions (economy and stability), whereas no significant impacts were observed in the remaining Pi-C dimension (social). In addition, the Pi-C forecasted that there would be no significant adverse impacts on the US construction industry caused by the pandemic until the end of 2022.