The shear behavior of prestressed concrete (PC) beams is a complex problem because there are many influential parameters involved. Currently, the code-based shear strength of PC beams is primarily based on empirical equations, which tend to be overconservative and are unable to generalize to different cases (e.g., beams with or without stirrups). This paper presents a framework to develop an explainable, data-driven model for the shear design of PC beams over a wide range of parameters. To this end, a comprehensive data set was assembled, consisting of 670 experiments of PC beams with and without stirrups. Different machine-learning (ML) techniques, including Random Forest, AdaBoost, and XGBoost, were evaluated to define a predictive model for the data set. Then, the most accurate model (based on XGBoost) was optimized to achieve high accuracy (with a coefficient of determination of 0.98 for the testing set). Moreover, the Shapley Additive exPlanations technique was used to explain and evaluate the importance of different parameters on the output of the predictive model. The predictive model was shown to be largely more accurate and more generalizable than current design equations and advanced finite-element analysis. In addition, reliability-based shear strength reduction factors were derived for the proposed ML model. These reduction factors allowed the application of the proposed ML model in the code-compliant shear design of PC beams.

This paper presents the results of an experimental investigation on the performance of half-scale, unbonded, post-tensioning, precast concrete walls subjected to fully reversed cyclic loads. Moreover, this paper discusses the damage progression and failure mechanism of each specimen, associated with their construction details (damper types and confinement details of the boundary elements). Five isolated walls were tested, each consisting of precast concrete panels joined only by unbonded post-tensioning strands. The bottom joint of the walls featured two kinds of dampers: Three specimens had mild steel reinforcement, crossing the bottom joint, and the other two specimens had external replaceable hysteretic dampers, attached to the wall sides. Different types of confinement details at the boundary elements were used among the specimens. Steel fibers were mixed into the concrete of one specimen. The prestressing load ratio was 0.05 in most of the specimens. An additional axial load of 468.5 kN was applied to each specimen (additional axial load ratio of about 0.04), before any lateral loading. Quasi-static, displacement-controlled loads were applied to the specimens until a significant strength reduction was observed. Although only moderate axial loads were applied, most of the specimens sustained drifts above 3% while maintaining their lateral strength, gravity load stability, and self-centering. A better performance was observed in the specimens with external dampers, with drifts above 4%, less residual drifts and less cover concrete spalling, particularly in the specimen with steel fibers. The external dampers were very effective in dissipating energy before fracture, until 3% drifts. On the other hand, the specimens with internal dampers sustained unexpected damage at their bottom joint, after reaching their peak strength. For instance, the gravity load-carrying capacity of one specimen with internal dampers was compromised after 4% drifts. Then, the use of external replaceable dampers provided higher performance and post-earthquake recovery for unbonded post-tensioned precast walls. Additional design recommendations are provided based on the test results.

This paper presents an analytical procedure to assess the monotonic (envelope) force-deformation response (capacity curve) of unbonded post-tensioning precast concrete (UPT) walls with internal and external replaceable dampers. This procedure, which can also be used to design UPT walls, was compared and validated against experimental evidence. A close agreement (strength discrepancies of 2% on average) was found between the response predictions and experimental results. This agreement was maintained until peak strength was achieved. Moreover, fiber-based models were implemented to further study the cyclic behavior of UPT walls designed according to the proposed analytical procedure. The numerical models were accurate in representing the global cyclic behavior of UPT walls in terms of strength, displacement capacity, and energy dissipation; nonetheless, these models showed some discrepancies in capturing the residual drifts of some UPT walls with internal dampers. A parametric analysis was also conducted to explore the influence of some design parameters on the deformation capacity and energy dissipation of UPT walls. It was found that the ratio of damper reinforcement is critical for the energy dissipation and self-centering capacity of UPT walls, so ratios of 0.10 to 0.25 should be provided to follow the limit states indicated in this paper. Based on the results, the analytical procedure and limit states presented in this paper are considered suitable for UPT walls with external replaceable dampers under total axial load ratios equal or lower than 0.125.

Accurate crack width measurement is essential for the condition assessment of reinforced concrete (RC) bridges, yet most unmanned aerial vehicle (UAV) inspections remain limited to pixel-level observations that cannot be converted into reliable metric units without an external scale reference. This paper presents a lightweight, drone-agnostic UAV-mounted metrology system that enables standards-aligned metric crack width measurement directly from inspection imagery. The payload integrates a focusable diffractive optical element (DOE) red laser that projects a cross pattern of known angular geometry, three TF-Luna time-of-flight (ToF) distance sensors, and an ESP-WROOM-32 microcontroller that provides dual-rate sampling, Bluetooth Low Energy (BLE) streaming, and on-board logging. A two-stage calibration links the synchronized distance measurements to the physical length of the projected cross, yielding an image-specific pixel-to-millimeter scale that is applied to pixel-level crack widths obtained from a vision-based segmentation pipeline. The system is field-deployed on the Puente Huamani Bridge in Pisco, Peru, where measurements of 39 cracks classified under AASHTO MBEI condition states are compared against independent manual measurements by six inspectors. The proposed system reduces measurement variability across all condition states (CS), lowering the average coefficient of variation from 0.36 to 0.10 for fine CS1 cracks, from 0.27 to 0.11 for CS2, and from 0.22 to 0.07 for CS3. Cross-platform adaptability is demonstrated through an additional deployment on a DJI Matrice 350 RTK at the Low Level Bridge in Edmonton, Canada. The results indicate that the system provides a practical, low-cost, and scalable solution for repeatable, standards-aligned UAV-based bridge crack assessment.