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Understanding the thermal-hydraulic-mechanical behavior of rock joints is essential in many civil and rock engineering applications, but controlled testing on natural joints is often impractical. This study investigates artificial tensile joints generated by wedge-splitting in medium- to coarse-grained granite specimens and evaluates the effects of loading rate, specimen size, and loading direction on surface morphology. Block and cylindrical specimens were tested under loading rates of 0.0001~0.01 mm/s. Joint surfaces were digitized via 3D laser scanning; directional profiles at 15 increments were used to compute three roughness parameters including CLA (Center Line Average), Z2 (root mean square of the first derivative of a profile), and fractal dimension (D). Spatial correlation was quantified using the experimental variogram with spherical fits to obtain range and sill. No discernible rate dependency was detected in either roughness or variogram parameters within the tested range. Size effects were parameter-specific: CLA increased with specimen size, whereas Z2 and D were relatively higher in the smallest specimens. Roughness showed a clear directional effect, being greater perpendicular to the loading axis than parallel. Variogram parameters increased with size (both range and sill), while directional differences in spatial correlation were weak and generally not statistically significant. Consistent with the amplitude-correlation relationship, the sill related to CLA, and a normalized initial slope (sill range) qualitatively related to Z2 and D. Overall, specimen size and loading direction govern the scale of roughness that is emphasized, providing practical guidance for the design and interpretation of experiments using artificial joint specimens.

This study evaluates the lateral capacity of flexible pre-bored precast concrete (PC) piles under drained soil conditions by employing various representative interpretation criteria. A database comprising 15 field lateral load tests in Taiwan was analyzed to investigate the relationships and reliability among various displacement, rotation, and graphical-based interpretation methods. Results show that the mean interpreted Q/QH ratios for displacement and rotation-based methods range from 0.20 to 0.80, increasing with larger movements. For graphical methods, the ratios are QS&W/QH = 0.42 and QL/QH = 0.59, with standard deviations and coefficients of variation comparable to other criteria. The QL method provided the most reasonable and consistent evaluation of both capacity and displacement, corresponding closely to the load at 20 mm or 3% of the pile diameter. Regression analysis identified Q10%B as the most reliable predictor, with a high coefficient of determination (r2 = 0.92). Overall, pre-bored PC piles exhibited higher normalized lateral capacities than drilled shafts and driven piles. The findings provide practical guidance for the consistent interpretation and design of flexible pre-bored PC piles under lateral loading.

Proper placement of load-bearing elements plays a critical role in earthquake-resistant building design. Design mistakes and deficiencies in the arrangement of columns, shear walls, and beams may reduce seismic performance, increase torsional effects, cause frame discontinuities, and lead to inefficient or uneconomical structural solutions. Therefore, early identification of such deficiencies directly from structural floor plans can provide important support during both preliminary design and rapid assessment stages. This study presents an automated methodology for detecting, classifying, and evaluating load-bearing elements in RC building floor plans by integrating image processing and deep learning techniques. The YOLO object detection algorithm was employed to identify structural components within floor plan images. A dataset comprising 500 RC building floor plans was developed, and structural elements were manually annotated and labeled for model training and validation. Unlike previous approaches that mainly focus on limited structural components, the proposed model detects columns, shear walls, and four different beam types according to their support conditions. In addition, detected bounding box coordinates are converted into real-world dimensions using grid distances obtained from drawings, allowing the cross-sectional dimensions of each element to be determined. The model achieved high detection performance, particularly for columns, shear walls, and main beams, with accuracy values exceeding 93%. The results show that the proposed approach can accurately obtain information from structural floor plans and provide a promising decision-support system.

This article proposes a method for detecting surface cracks in building concrete based on improved YOLOv8. By introducing deformable attention mechanism (DAttention) in the backbone network, the crack growth trend can be dynamically focused; Enhance multi-scale feature expression capability in neck design (Cross scale Feature Fusion Module, CCFM); Embedding Efficient Channel Attention (ECA) in the head to enhance the weight of key features; And replace the Complete Intersection over Union (CIoU) loss function with the Scale invariant Intersection over Union (SIoU) loss function to optimize the bounding box regression process. The experimental results show that our method achieved a detection accuracy of 88.4%, a recall rate of 95.2%, and an average precision mean (mAP) of 96.4% on a self built dataset, which is significantly improved compared to the benchmark YOLOv8 model. This method is capable of extracting crack features in a complete and continuous manner, effectively identifying subtle cracks and suppressing background interference, providing reliable technical support for the health assessment and safety risk prevention of building structures.

Vibration-based Structural Health Monitoring (SHM) systems are susceptible to false alarms, particularly due to environmental influences. This study proposes that nonlinear static pushover Finite Elements (FE) analysis outputs can be used as complementary data to existing methods in Smart Building (SB) decision support systems. The proposed hybrid approach has the potential to reduce false alarm rates compared to decision-making based solely on modal parameter changes, statistical methods, and machine learning. To this end, in addition to these approaches, a secure proposal based on traditional procedures for integrating nonlinear structural analysis results into intelligent SHM systems is presented. A global performance evaluation of a core system was conducted to obtain local damage distribution and degrees at the element level. In the SHM system, the focus was on using element damage level thresholds obtained from system analysis as threshold values, rather than general limits in standards or solely the results of the structural system's specific global performance analysis. Today, thanks to advancements in hardware and software technologies, performing and disseminating these analyses is much easier than in the past. In the near future, the information obtained from these analyses will inevitably be further utilized in SHM systems. Although the study did not include experimental validation and model updating, it was shown that using the proposed physical thresholds in conjunction with existing modal-based SHM methods has the potential to reduce false alarm rates and provides a theoretical basis for rapid decision-making mechanisms after earthquakes.