The integration of drones in the construction industry has ushered in a new era of efficiency, accuracy, and safety throughout the various phases of construction projects. This paper presents a comprehensive overview of the applications of drones in the construction industry, focusing on their utilization in the design, construction, and maintenance phases. The differences between the three different types of drones are discussed at the beginning of the paper where the overview of the drone applications in construction industry is then described. Overall, the integration of drones in the construction industry has yielded transformative advancements across all phases of construction projects. As technology continues to advance, drones are expected to play an increasingly critical role in shaping the future of the construction industry.

The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

The demand for technologies related to smart construction is rising as the need for increased productivity in the field of construction becomes ever more important. The fourth industrial revolution has accelerated the growth of advanced technologies, including artificial intelligence, 5G, the internet of things, and more, and these technologies are key in relation to smart construction. During the construction phase of our study, a truck travelled along a temporary road within a construction site. Thus, optimizing the location of the temporary road enhances the truck’s moving path, resulting in increased productivity in the road construction process. Thus, in this work, the concept of automating a path for construction equipment (a truck) is proposed. The construction site was divided into cells, where five factors were suggested to create a cost model that could automatically be used to create one of the most efficient paths for construction equipment. With the proposed concept, one can automatically create one of the most efficient paths when deciding the location for a temporary road during construction.

The construction industry is continuously evolving, seeking innovative solutions to enhance efficiency, reduce costs, and improve safety. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a transformative technology in the construction sector, offering numerous advantages in data collection and site management. This paper presents a novel approach for utilizing UAVs to automate the path generation of rollers, a crucial element in the construction of roads and other large-scale infrastructure projects. A UAV was used to scan the target area to create a model; the next step was to generate the path for the rollers. Traditionally, the process of determining optimal roller paths is labor-intensive and reliant on manual surveys and engineering expertise. This study proposes a streamlined workflow that harnesses UAVs equipped with computer vision technology to capture high-resolution topographical data of construction sites. This data is then processed through an algorithm created by the authors that automatically generates optimized roller paths based on several factors.

Large and complex construction projects require a timely supply of resources to reduce the construction time as well as prepare a construction plan that secures the maximum economic benefit. Because most large-scale construction sites are isolated from downtown areas, problems with the supply and demand of specific resources are frequently encountered. Previous plans with respect to resource allocation are based on uniform resource mobilization at the initial stage of construction, which is an insufficient resource supply and demand model for construction sites that have changing needs throughout the phases of construction. In this study, an active resource allocation method that the level of actual resource mobilization can be changed by the construction period is demonstrated, enabling the optimization of resource utilization to the construction site. The schedule change of resource allocation within the float time, the methods for moving a start date, the decision to increase construction time, and reasons for adjusting the width of the float time are presented in accordance with their risk level. A genetic algorithm facilitates the searching of an optimal construction schedule within the resource mobilization plan, and a case study with an example project is performed for verification.