The use of UAV (unmanned aerial vehicle) platforms and photogrammetry in bathymetric surveys has been established as a technological advancement that allows these activities to be conducted safely, more affordably, and at higher accuracy levels. This study evaluates the error levels obtained in photogrammetric UAV flights, with measurements obtained in surveys carried out in a controlled water body (pool) at different depths. We assessed the relationship between turbidity and luminosity factors and how this might affect the calculation of bathymetric survey errors using photogrammetry at different shallow-water depths. The results revealed that the highest luminosity generated the lowest error up to a depth of 0.97 m. Furthermore, after assessing the variations in turbidity, the following two situations were observed: (1) at shallower depths (not exceeding 0.49 m), increased turbidity levels positively contributed error reduction; and (2) at greater depths (exceeding 0.49 m), increased turbidity resulted in increased errors. In conclusion, UAV-based photogrammetry can be applied, within a known margin of error, in bathymetric surveys on underwater surfaces in shallow waters not exceeding a depth of 1 m.

A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset consists of 1046 images selected from video footage by a frame extraction algorithm and txt files containing the objects' class and coordinates information. These data can be used to develop computer vision techniques in the engineering and construction fields.

The combination of light detection and ranging (LiDAR) sensors and unmanned aerial vehicle (UAV) platforms have garnered considerable interest in recent years because of the wide range of applications performed through the generation of point clouds, such as surveying, building layouts and infrastructure inspection. The attributed benefits include a shorter execution time and higher accuracy when surveying and georeferencing infrastructure and building projects. This study seeks to develop, integrate and use a LiDAR sensor system implemented in a UAV to collect topography data and propose a procedure for obtaining a georeferenced point cloud that can be configured according to the user’s needs. A structure was designed and built to mount the LiDAR system components to the UAV. Survey tests were performed to determine the system’s accuracy. An open-source ROS package was used to acquire data and generate point clouds. The results were compared against a photogrammetric survey, denoting a mean squared error of 17.1 cm in survey measurement reliability and 76.6 cm in georeferencing reliability. Therefore, the developed system can be used to reconstruct extensive topographic environments and large-scale infrastructure in which a presentation scale of 1/2000 or more is required, due to the accuracy obtained in the work presented.

The introduction of artificial intelligence methods and techniques in the construction industry has fostered innovation and constant improvement in the automation of monitoring and control processes at construction sites, although there are areas where more studies still need to be conducted. This paper proposes a method to determine the criticality of cracks in concrete samples. The proposed method uses a previously trained YOLOv4 neural network to identify concrete cracks. Then, the region of interest, determined by the bounding box resulting from the neural network model classification, is extracted. Finally, the extracted image is converted to negative grayscale to quantify the number of white pixels above a certain threshold, automatically allowing the system to characterize the fracture’s extent and criticality. The classification module reached a veracity between 98.36% and 99.75% when identifying five concrete crack types of failures in 1132 images. A qualitative analysis of the results obtained from the characterization module shows a promising alternative to evaluate the criticality of concrete cracks.

Undergraduate programs face the challenge of staying up-to-date by responding to globalization, internationalization, and regionalization processes. Civil engineering programs should meet the requirements of the Architecture, Engineering, Construction, and Operation (AECO) industry. Building Information Modelling (BIM) is a methodology that involves shared digital representations of built assets to facilitate design, construction, and operation processes within the construction knowledge field. The present research describes a methodology with six mechanisms to develop and consolidate BIM-related competencies within the construction area from an undergraduate civil engineering program. It also explores students’ perceptions about the subjects within this area and their relationship with the training and reinforcement of the competencies. The study follows a descriptive scope with an action research design. The principal result is the methodology, which has six mechanisms: (1) BIM implementation into topics and syllabi; (2) use of collaborative platforms; (3) professor training programs; (4) establishment of student organizations; (5) development of a BIM application project; and (6) area and subject meetings. The validation process, which included professors, enrollment, employability and research rates, and the curricular proposal and students’ perceptions, revealed that the mechanisms could effectively support the development and reinforcement of BIM-related competencies.

The construction industry is increasingly incorporating artificial intelligence into processes for the efficiency and accuracy of structural analysis and monitoring. However, obtaining high-quality datasets to train algorithms for detecting concrete cracks in structural components remains challenging, as such cracks normally develop over an extended period under real-world conditions. We introduce a curated dataset of 1,132 manually classified images of concrete cracks in beams and columns. These images were captured in a controlled laboratory environment using a static IP camera and annotated using the LabelImg tool. The dataset includes five object classes representing distinct cracks and failures in beams and columns and corresponding.txt files containing classification and coordinates data. This dataset is designed to facilitate developing and validating of neural network-based computer vision models for automated crack detection. It is a very useful resource for researchers in structural engineering, which enables further developments in automated structural health monitoring and contributes to the overall use of AI in the construction industry.