Structure and Infrastructure Engineering

This study introduces an analytical framework and case study to evaluate driving conditions and safety of vehicles on highway bridges subjected to seismic excitation. The driving conditions, namely, driver perceptibility and comfort were examined using overall vibration total value (OVTV) criterion defined in the ISO 2631-1, while driver reaction was simulated using second-order predictable-correction (SOPC) model. A case study was presented using 3D-finite element (FE) model of an existing highway interchange and vehicle-bridge-seismic dynamics (VBSD) model. In this case study, site-specific ground motions at two intensity levels were used to simulate bridge responses. Influences of earthquake intensity, bridge geometry, and road conditions were investigated on five non-articulated vehicles: light vehicle, SUV, small truck, bus, and large truck. Simulations reveal that the driving comfort level for most vehicles has surpassed the uncomfortable and extremely-uncomfortable level defined in the ISO standard under L1 earthquake and L2 earthquake, respectively. Driving conditions worsened on a curved bridges compared to straight bridges. Three driver reaction scenarios were simulated to examine risks and safety indices. The results showed that deceleration action was the most effective response, followed by a combination of deceleration and changing course, while changing course only was the least favorable outcome.

Unmanned aerial vehicles (UAVs) have the potential to reduce bridge inspection time and cost while increasing safety. However, UAV-collected field data has inherent properties that complicate damage assessment. In this article, the authors integrate UAV-collected imagery data with automatic defect detection to create a novel first-pass bridge inspection algorithm, which aims to conduct an initial corrosion assessment to determine if further inspection is needed. The authors use UAV-captured images of bridges near Atlanta, Georgia, USA, to create a dataset representative of bridge inspections, including the presence of chaos and misleading objects. The proposed methodology integrates deep learning methods (fully convolutional network (FCN)) to remove natural elements in the image background that resemble corrosion, image processing techniques to quantify texture and reduce lighting effects, and unsupervised learning (K-means) for corrosion segmentation. Experimental results show that the K-means algorithm outperforms other segmentation methods, including image thresholding and deep learning, with a recall of 0.78 and mIoU of 0.72 on UAV-collected field data. Thus, the newly developed method is a promising tool to improve the efficiency and safety of bridge inspections by reducing the number of full inspections conducted on structurally sound bridges.

For long-span cable-stayed bridges, the unpredictable damages and complex structure result in significant challenges of solving displacement and quantifying the uncertainty of structural displacement. An effective approach to solve structural displacement and quantify its uncertainty is proposed based on the multiple response surfaces method. Taking a long-span cable-stayed bridge in China as an example, the main steps are illustrated. First, the damage modes of the materials, members, and structures are determined to establish the corresponding damage uncertainty analysis models. Depending on the combinations of different damaged members, 13 typical damaged conditions are selected for the structural damage mode. After that, a surrogate model for solving the structural displacement is built based on the multi-response surfaces method and uncertainty parameters of damaged materials and other factors. Subsequently, the displacement under corresponding damage conditions is calculated, the uncertainty of the structural displacement is quantified with MCS and Sobol method. Numerical results showed that the variation coefficient of the structural displacement is about twice that of the damaged material elastic modulus, and the damages do have a larger influence on the variation of displacement. Additionally, the proposed method can solve the structural displacement efficiently and be well applied to the long-span structures.

This comprehensive review investigates the integration of Building Information Modeling (BIM) and Geographic Information System (GIS) in infrastructure projects. The study begins with a historical overview of BIM and GIS, outlining their individual contributions to construction and geographical data management. It then delves into the synergistic benefits of integrating these technologies, emphasizing enhanced accuracy and efficiency in infrastructure planning and design. Various methodologies for effective BIM-GIS integration are examined, including interoperability and data sharing protocols. The study identifies key challenges in this integration, such as technical limitations, organizational barriers, and the need for standardized practices. These challenges are analyzed in the context of current industry practices and technological advancements. Furthermore, the paper highlights the crucial role of interdisciplinary collaboration in overcoming these hurdles. Looking forward, the review proposes future research directions, advocating for more innovative approaches to fully exploit the potential of BIM-GIS integration. These include the development of more robust integration frameworks, fostering closer collaboration between industry and academia, and leveraging emerging technologies to streamline BIM-GIS workflows. The review concludes by underscoring the transformative impact of BIM-GIS integration on the future of sustainable and efficient infrastructure development. ARTICLE HISTORY