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To solve the problems of measurement errors led by mismatches of dense feature matching in machine vision structural deflection measurement, this paper proposes a dense feature extraction, matching, and dual-step mismatch-removal-based full-field structural dynamic deflection measurement method. First, the of dense feature detection and matching theory is introduced to extract the SIFT feature points on a structural surface in an image sequence and matched by FLANN to trace the structure movement, and the mechanisms and causes of mismatches are analyzed. Then, a dual-step mismatch removal method combining RANSAC and Structural Displacement Continuity Restriction (SDCR) is introduced to achieve full-field dynamic beam deflection measurement. The proposed method is validated through indoor cantilever beam experiments, and results show that the method can effectively eliminate a large number of SIFT feature mismatches (accounting for approximately 55% of the total matched feature points). The full-field dynamic displacement field of the beam can be measured with the correctly matched dense feature points by converting dense feature point displacements into continuous and uniform spatiotemporal deflections of the structure. A comparison with the GOM Correlate Professional DIC measurement system was conducted, and the maximum measurement error of the cantilever beam dynamic displacement of the proposed method is between 0.024 and 0.053 mm, the root mean squared error of displacement is approximately 0.01 mm, and the correlation coefficient between two deflection–time curves reaches 0.9964. The proposed algorithm is proven to be effective in full-field displacement measurement and has great potential in future structural health monitoring of bridges.

To solve the problem of low accuracy in automatic concrete crack image segmentation and the non-standardization of concrete crack image datasets, an exposure-based concrete crack image capture scene characterization method was proposed, and the optimal exposure interval for crack segmentation was presented by multiple scene image capture experiments. First, current public crack datasets were collected and analyzed, and it was shown that improper spatial resolution, mislabeling, overexposure, and defocus are frequent non-standardization problems in crack dataset production. Through the analysis of the photoelectric principle in concrete crack imaging, an equivalent exposure was set as a core indicator for scene characterization. Twenty-one indoor scenes were designed by varying the illumination intensity and exposure time, and the experimental results showed that an equivalent exposure can be a core control index for scene characterization. The grayscale distribution law of concrete crack images was analyzed with four specimens’ images captured indoors in 50 exposure scenes, and the segmentation accuracy of an image from each scene was calculated through comparison with corresponding manually labeled binary files. The experiment’s results revealed that 5~50 lx·s was the optimal equivalent exposure interval for concrete crack image segmentation, in which better segmentation accuracy was achieved with an F1 score of up to 96.3%.

To improve the accuracy of concrete crack measurement with a machine vision method in structural health monitoring and in technical status evaluation, a subpixel crack measurement method based on the partial area effect was proposed. (1) First, a pixelwise crack image segmentation method was established through a multi-step process of multi-threshold fusion and morphology operation, and a novel pixel degree crack width calculation method was developed with the extraction of the middle points, the center line and its normal, and the intersection of the center line normal and crack edges. (2) Then, a subpixel algorithm based on the partial area effect was introduced to locate vertical, horizontal, and oblique cracks in subpixel crack edges, and the subpixel crack width could be calculated along the crack center line pixelwise. (3) Finally, the proposed method was verified by indoor concrete beam crack measurement tests with a digital microscope, and the results show that the maximum relative errors of the subpixel width of the horizontal, vertical, and oblique straight cracks measured by the proposed method were 3.06%, 8.97%, and 5.16%, respectively. The absolute error of the crack length was less than 0.30 mm, and the measurement accuracy could reach 0.01 pixels. The subpixel crack measurement method provides a novel possible solution for structural health monitoring.

Basalt fiber-reinforced concrete (BFRC) represents a form of high-performance concrete. In structural design, a 28-day resting period is required to achieve compressive strength. This study extended an extreme gradient boosting tree (XGBoost) hybrid model by incorporating genetic algorithm (GA) optimization, named GA-XGBoost, for the projection of compressive strength (CS) on BFRC. GA optimization may reduce many debugging efforts and provide optimal parameter combinations for machine learning (ML) algorithms. The XGBoost is a powerful integrated learning algorithm with efficient, accurate, and scalable features. First, we created and provided a common dataset using test data on BFRC strength from the literature. We segmented and scaled this dataset to enhance the robustness of the ML model. Second, to better predict and evaluate the CS of BFRC, we simultaneously used five other regression models: XGBoost, random forest (RF), gradient-boosted decision tree (GBDT) regressor, AdaBoost, and support vector regression (SVR). The analysis results of test sets indicated that the correlation coefficient and mean absolute error were 0.9483 and 2.0564, respectively, when using the GA-XGBoost model. The GA-XGBoost model demonstrated superior performance, while the AdaBoost model exhibited the poorest performance. In addition, we verified the accuracy and feasibility of the GA-XGBoost model through SHAP analysis. The findings indicated that the water–binder ratio (W/B), fine aggregate (FA), and water–cement ratio (W/C) in BFRC were the variables that had the greatest effect on CS, while silica fume (SF) had the least effect on CS. The results demonstrated that GA-XGBoost exhibits exceptional accuracy in predicting the CS of BFRC, which offers a valuable reference for the engineering domain.

Split Hopkinson pressure bar (SHPB) tests are usually used to determine the dynamic mechanical strength of basalt-fiber-reinforced concrete (BFRC), but this test method is time-consuming and expensive. This paper makes predictions about the dynamic mechanical strength of BFRC by employing machine learning (ML) algorithms and feature sets drawn from experimental data from prior works. However, there is still the problem of improving the accuracy of the dynamic mechanical strength prediction by the BFRC, which remains a challenge. Using stacking ensemble learning and genetic algorithms (GA) to optimize parameters, this study proposes a prediction method that combines these two techniques for obtaining accurate predictions. This method is composed of three parts: (1) the training uses multiple base learners, and the algorithms employed by the learners include extreme gradient boosting (XGBoost), gradient boosting (GB), random forest (RF), and support vector regression (SVR); (2) multi-base learners are combined using a stacking strategy to obtain the final prediction; and (3) using GA, the parameters are optimized in the prediction model. An experiment was conducted to compare the proposed approach with popular techniques for machine learning. In the study, the stacking ensemble algorithm integrated the base learner prediction results to improve the model’s performance and the GA further improved prediction accuracy. As a result of the application of the method, the dynamic mechanical strength of BFRC can be predicted with high accuracy. A SHAP analysis was also conducted using the stacking model to determine how important the contributing properties are and the sensitivity of the stacking model. Based on the results of this study, it was found that in the SHPB test, the strain rate had the most significant influence on the DIF, followed by the specimen diameter and the compressive strength.

To investigate the influences of the chemical corrosion on the dynamic fracture mechanical properties and failure characteristics of marble, the dynamic fracture test for the notched semicircular bend (NSCB) marble specimens with different degrees of corrosion damage was conducted using a split Hopkinson pressure bar. The results show that both the porosity and corrosion damage variable Dc characterized by porosity increase as corrosion time T increases. The dynamic fracture toughness decreases linearly from 5.22 to 2.15 MPa m1/2 as Dc increases from 0 to 4.42%. This is because the corrosion damage done to interior structure leads to a reduction of cohesion and internal friction angle inside the specimen. In the Dc range of 0–4.42%, the fractal dimension of the main crack propagation path increases from 1.47 to 1.59, by an increase rate of 8.16%. Both the joint roughness coefficient and the actual area of the fracture surface increase as Dc increases. Due to the degradation of the connected force between the rock particles inside the specimen, there is an obvious decrease in the fracture energy per unit fracture surface from 0.94 to 0.38 J cm−2, with a decrease rate of 59.39%. With T increases, the contents of CaO, MgO, and Al2O3 in specimen increase, indicating that the degree of corrosion damage increases.

Rock burst is a catastrophic phenomenon that often occurs in underground rock mass engineering. In order to reveal the essence of rock burst of a hard roof in the process of roadway excavation, the particle discrete element method is used to establish a roadway model and simulate the disturbance of harmonic dynamic load based on the analysis of a rock burst accident in a deep mine. The crack field, stress field, displacement field, and kinetic energy of roadway surrounding rock disturbed by cyclic dynamic load were analyzed, and the disaster mechanism of roadway impacting roof instability was discussed. The results show that, compared with the roadway support structure under static load that can give full play to its control function of surrounding rock, the roadway surrounding rock will collapse and lose stability in a large area under the roof cyclic dynamic load, and the ordinary supporting structure cannot give full play to its control function of surrounding rock, resulting in the surrounding rock destruction and supporting structure failure. In addition, the essence of rock burst in a hard thick roof is due to the instantaneous superposition of static stress and dynamic load, leading to the instantaneous instability and collapse of roadway roof in a large area. The research is of great significance to further understand the deformation and failure mechanism of roadway surrounding rock under strong impact load, to guide the safe production and prevent the occurrence of rock burst hazard in underground rock mass engineering.