Chuang Cui

Three-dimensional point-cloud measurement can resolve corrosion topography with high spatial fidelity, but converting measured data into reproducible and quantitatively validated surface representations remains challenging. This study proposes a point cloud–driven multi-feature-constrained fractal reconstruction framework for corrosion morphology reconstruction and intermediate-state prediction of weathering steel surfaces. Based on measured point-cloud data, a descriptor system is established by combining ISO 25178 areal parameters, fractal and multifractal descriptors, spatial statistical metrics, and corrosion-pit morphology descriptors. Five key parameters, namely the fractal dimension D, arithmetic mean height Sa, skewness Ssk, kurtosis Sku, and autocorrelation length Sal, are used as core constraints in a multi-objective iterative reconstruction strategy. The framework is benchmarked against the Gaussian random field, Weierstrass–Mandelbrot, spectral synthesis, and Diamond–Square methods. Measured surfaces at different corrosion stages show substantially improved multi-parameter consistency and height-distribution agreement, with Kolmogorov–Smirnov p-values of 0.05–0.69 for reconstructed surfaces. A time-dependent parameter evolution model is further introduced to support morphology prediction at unmeasured intermediate time points, and a Monte Carlo uncertainty-quantification module is used to evaluate reconstruction variability and identify dominant contributors to uncertainty propagation. The proposed framework provides a measurement-oriented route from point-cloud acquisition to descriptor-based reconstruction, statistical validation, and uncertainty-aware intermediate-state estimation.

I-beams are widely used in industrial structural engineering, but their flanges are prone to localized damage during service. Ultrasonic guided waves have long-distance propagation capabilities in cross-section steel structures, making them suitable for damage monitoring of I-beams. However, existing test approaches rely on multiple piezoelectric sensors arranged into arrays, which may exhibit inconsistencies due to factors such as contact errors, measurement point locations, and connecting wires. To address this challenge, this paper develops an ad-hoc piezoelectric guided wave sensor for flange damage monitoring and assessment of hot rolled I-beams. The design process involves the analysis of dispersion characteristics, modal shapes, and energy distribution, the selection of a guided wave frequency suitable for flange monitoring of I-beams, and the regulation of the sensor’s resonant frequency. The results show that the designed sensor is capable of excitation and reception of ultrasonic guided waves in the flange with modal shapes dominated by the first-order mode. This method exhibits good sensitivity to flange cracks in I-beams with a damage rate ρ > 1.5%, providing a feasible solution for localized damage monitoring in cross-section steel structures.

Coating-based sensors have demonstrated great potential for fatigue crack monitoring in the aerospace industry. However, their application in steel bridges remains limited due to constraints in fabrication methods, sensing principles, and cost-effectiveness. This study proposes a low-cost, scalable crack monitoring system tailored for steel bridge structures, achieving real-time crack tracking and geometric reconstruction through material optimization, innovative sensor design, and streamlined system integration. A self-developed conductive ink balances formulation simplicity with high performance, offering reliable conductivity and environmental stability under harsh conditions. The sensor is fabricated via screen-printing, enabling rapid and cost-effective production. Powered by an STC89C52 microcontroller and supported by Python-based processing and Matplotlib rendering, the system allows real-time visualization of crack paths. Experimental results under various crack patterns and temperatures confirm the system’s robustness, achieving a monitoring resolution of 0.8 mm, direction and length identification errors below 1 mm, and stable performance across a 0°C–100°C temperature range. These findings demonstrate the system’s strong potential for practical deployment in steel bridge crack monitoring.

Flexible strain sensors fabricated using spray coating enable efficient and reliable strain monitoring of steel bridges. This study aimed to develop a flexible strain sensor with an extremely low detection limit, high linearity, and high sensitivity. The sensing layer materials were prepared by simply mixing silver-coated copper powder particles and water-based acrylic emulsion. Commercial moisture-proof insulating paint for printed circuit boards was used as the insulating layer, and modified polyurethane resin was used as the protective layer. Sensors were fabricated using the spray coating process, and the influence of the conductive-filler content on their static sensing performance was investigated. The results showed that the sensor exhibited the highest sensitivity near the percolation threshold, with a gauge factor of 272.54 and minimum strain detection limit of 0.000 98% but suffered from high resistance relaxation and hysteresis. At higher conductive-filler contents, the resistance relaxation and hysteresis decreased; however, the sensitivity decreased, and the minimum detection limit of strain increased. The linear correlation coefficient of the sensor first increased and then decreased with increasing filler content; however, all values were above 0.940. A comparison of the conductivity and sensing performance revealed that the performance changes were significant near the percolation threshold and stabilized away from it, whereas the linearity coincided with the conductivity changes. This indicated that the sensor performance was closely related to the initial conductive network state of the sensing layer.

Monitoring stress and strain at the critical details of steel bridges is essential for ensuring structural integrity. This study introduces a three‐layer flexible strain sensor produced through a spraying process, using flake‐shaped silver‐coated copper powder as the conductive filler and modified acrylic emulsion as the matrix material. The study investigated the impact of size parameters on sensor sensitivity, determining optimal dimensions of 20 mm in length, 2 mm in width, and an initial resistance value ranging from 1.0 Ω to 1.8 Ω. Analysis of the optimized sensor’s performance unveiled high sensitivity and linear response capabilities under low strain conditions with a gauge factor (GF) value of up to 25.6 and a linear correlation coefficient R² ≥ 0.971 under 300 με. Notably, the sensor exhibits an extremely low strain detection limit of 0.005% and a broad response range spanning from 0.005% to 0.19% strain. It demonstrates swift response and recovery times of 500–800 ms, showcases directional strain response, exhibits good repeatability, and endures durability tests (withstanding 3000 cycles). Furthermore, a fitting formula is proposed to accurately depict the strain and relative resistance change relationship across a wide response range. The study and initial application of this sensor’s sensing characteristics and performance signify its potential for practical engineering applications.

In this study, an autonomous computer vision-based method is presented to quantitatively detect loose bolts. The method integrates keypoint detection via YOLOv5 and PIPNet, distortion correction via perspective transformation, and rotation angles quantification via geometric imaging. Distortion correction is incorporated to address skewed angles and improve the accuracy of rotation angles. A representative experiment on bolted connection of steel structures is conducted to evaluate the presented approach. The effects of the focal distance, skewed angle, and lighting conditions on the detection and quantification performance are evaluated by varying the imaging conditions. The results demonstrate that the presented approach automatically detects all bolts and their corners, irrespective of the imaging conditions. No false detection occurs, and the quantification errors are lower than 1°. The proposed method can be deployed for automatic detection of loose bolts and quantification of rotation angles in bolted connections under different imaging conditions.

Orthotropic steel deck (OSD) is widely used in long-span cable-stayed bridges, but vehicle-induced dynamic stresses at its deck-to-rib (DTR) joints often make OSD vulnerable to local failure or fatigue damage. This paper presents a framework for vehicle-induced dynamic stress analysis of DTR joints of OSD in a cable-stayed bridge. The multi-scale finite element model of the bridge is first developed using solid elements to simulate DTR joints of OSD interacted with pavement (local model), shell elements to simulate the most vulnerable girder segment (intermediate model), and beam/truss elements to simulate the rest part of the bridge (global model). Road vehicles are modelled as a combination of several rigid bodies connected by a series of springs, dampers and pivots. The vehicle-induced dynamic stress responses at the DTR joints of a long-span cable-stayed bridge are finally computed to assess the effects of bridge dynamics, vehicle dynamics, asphalt pavement temperature, roughness and vehicle velocity. The results show that the dynamically coupled vehicle-bridge system should be considered and that pavement temperature and roughness are two profound factors affecting the stress responses of DTR joints.