The structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.

Sustainability has emerged as a prominent concern globally, extending its influence into various domains, including education. It is recognized as of utmost importance to address global environmental challenges. However, there is a critical gap in the perception of innovative teaching strategies, i.e., interdisciplinary collaboration, experiential learning, and targeted approaches, to improve sustainability literacy and its applications. This review analyzes existing environmental and sustainability education frameworks and approaches to determine desired learning outcomes and challenges associated with sustainability education. Also, it explores and identifies concepts, theories, and assumptions found within the literature review, promoting sustainability integration within engineering education. The review was conducted to facilitate the development and improvement of sustainability education within the Architectural Engineering discipline, a field known for emphasizing educational innovation and technical excellence. By synthesizing existing ideas related to sustainability and sustainable development, this work aims to guide curriculum designers and educators in fostering sustainability competencies among engineering students within the built environment.

The rising frequency of natural disasters demands efficient and accurate structural damage assessments to ensure public safety and expedite recovery. Human error, inconsistent standards, and safety risks limit traditional visual inspections by engineers. Although UAVs and AI have advanced post-disaster assessments, they still lack the expert knowledge and decision-making judgment of human inspectors. This study explores how expertise shapes human–building interaction during disaster inspections by using eye tracking technology to capture the gaze patterns of expert and novice inspectors. A controlled, screen-based inspection method was employed to safely gather data, which was then used to train a machine learning model for saliency map prediction. The results highlight significant differences in visual attention between experts and novices, providing valuable insights for future inspection strategies and training novice inspectors. By integrating human expertise with automated systems, this research aims to improve the accuracy and reliability of post-disaster structural assessments, fostering more effective human–machine collaboration in disaster response efforts.