Background: We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). Methods: A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). Results: Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996–0.999) and 0.946 (95% CI, 0.906–0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. Conclusion: The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination.

With the influence of intense solar radiation heat and the greater temperature difference between day and night, surface concrete with a drastic temperature change can easily experience a great nonlinear temperature difference, which increases the risk of early-age concrete cracking. In this study, a distributed optical fiber temperature sensing (DTS) system is used to monitor the surface temperature gradient of concrete in real time, and a solar radiation heat monitoring test is also carried out based on the Baihetan project. Based on this, a solar radiation loading model and a finite element model of a typical pouring block considering solar radiation are established. Combined with the measured temperature data and different calculation conditions, the surface temperature changes of medium-heat and low-heat concrete experiencing solar radiation are analyzed, and the temperature control effect of surface concrete with different surface insulation measures is further analyzed. The results show that the temperature variation of medium-heat concrete at the same depth is more obvious than that of low-heat concrete. Additionally, the temperature variation of low-heat concrete is noticeable within 20 cm of the top surface. In addition, in an intense solar radiation environment, covering the concrete with a 4- or 5-centimeter-thick polyethylene coil can effectively control the surface temperature gradient and maximum daily amplitude of low-heat concrete, and surface concrete cured by running water has a significant temperature control effect. Therefore, it is suggested that 22–24 °C water temperatures be used for water curing during periods of intense solar radiation during the day and a 4-centimeter-thick polyethylene coil be used for coverage at night. These study results have been employed in the Baihetan project to optimize the temperature control scheme of the pouring blocks.

The accurate prediction of concrete temperature during arch dam construction is essential for crack prevention. The internal temperature of the poured blocks is influenced by dynamic factors such as material properties, age, heat dissipation conditions, and temperature control measures, which are highly time-varying. Conventional temperature prediction models, which rely on offline data training, struggle to capture these time-varying dynamics, resulting in insufficient prediction accuracy. To overcome these limitations, this study constructed a sparrow search algorithm–incremental support vector regression (SSA-ISVR) model for online concrete temperature prediction. First, the SSA was employed to optimize the penalty and kernel coefficients of the ISVR algorithm, minimizing errors between predicted and measured temperatures to establish a pretrained initial temperature prediction model. Second, untrained samples were dynamically monitored and incorporated using the Karush–Kuhn–Tucker (KKT) conditions to identify unlearned information, prompting model updates. Additionally, redundant samples were removed based on sample similarity and error-driven criteria to enhance training efficiency. Finally, the model’s accuracy and reliability were validated through actual case studies and compared to the LSTM, BP, and ISVR models. The results indicate that the SSA-ISVR model outperforms the aforementioned models, effectively capturing the temperature changes and accurately predicting the variations, with a mean absolute error of 0.14 °C.

A concrete production system (CPS) fault in dam engineering is one of the important factors influencing dam construction quality, which may directly affect the concrete-pouring construction progress and construction efficiency of the dam, and can even cause construction quality defects in the dam body. Reasonable classification and identification are of great significance to ensure the construction progress and quality of concrete dams. In this study, based on the concrete production logs of multiple concrete dams and literature reviews, a fault classification system for a CPS is proposed by comprehensively considering its mechanical structure characteristics and operating characteristics. The faults of the CPS are divided into 4 large categories and 22 subcategories. Additionally, the causes of CPS faults are summarized as human factors, environmental factors, mechanical component service life factors, and other factors. Based on the grey correlation analysis (GCA) method, a fault identification model of the CPS is established. With the actual production system fault statistical data of Shatuo hydropower station, the correlation coefficients for the four types of faults and the four influencing factors are calculated to determine the key faults of the CPS. The research results of the case study show that the service life factors of mechanical components have the greatest impact on batching metering system faults and mixer faults, with high grey correlation degrees of 84.66% and 76.85%, respectively. Environmental factors have the greatest impact on material delivery system faults and pneumatic system faults, with high grey correlation degrees of 90.81% and 94.9%, respectively. This paper provides theoretical support for the realization of fault pattern recognition of CPSs and provides a guiding reference for targeted fault handling.

To better understand the temperature changes in face slab concrete and address challenges such as delayed curing and outdated methods in complex and variable environments, this study investigates the use of visualization and real-time feedback control in concrete construction. The conducted study systematically develops an intelligent curing control system for face slab concrete based on multi-source measured data. A tailored multi-source data acquisition scheme was proposed, supported by an IoT-based transmission framework. Cloud-based data analysis and feedback control mechanisms were implemented, along with a decoupled front-end and back-end system platform. This platform integrates essential functions such as two-way communication with gateway devices, data processing and analysis, system visualization, and intelligent curing control. In conjunction with the ongoing Maerdang concrete face rockfill dam (CFRD) project, located in a high-altitude, cold-climate region, an intelligent curing system platform for face slab concrete was developed. The platform enables three core visualization functions: (1) monitoring the pouring progress of face slab concrete, (2) the early warning and prediction of temperature exceedance, and (3) dynamic feedback and adjustment of curing measures. The research outcomes were successfully applied to the intelligent curing of the Maerdang face slab concrete, providing both theoretical insight and practical support for achieving scientific and precise curing control.

The cable crane is the core hoisting equipment for high arch dam construction, and its hoisting trajectory is critical for both operational efficiency and safety. However, current trajectory planning does not adequately consider the underactuated characteristics of the cable crane. For instance, sudden stops or abrupt changes in direction can easily induce large swings of the bucket, causing safety risks and equipment wear. To address this issue, this paper developed a trajectory planning model for obstacle avoidance with smooth transitions in cable crane hoisting for arch dams and solved the high-dimensional optimization problem using a path–velocity decoupling strategy. First, a shortest path with geometrical conciseness and free collision was generated based on an improved A* algorithm to reduce the frequency of directional changes. Next, for different hoisting scenarios, segmented S-curve and polynomial velocity functions were proposed to ensure smooth velocity transitions. Then, an orthogonal experimental design was employed to generate a cluster of candidate trajectories that meet kinematic constraints, from which the optimal trajectory was selected using a multi-objective evaluation function. The results demonstrate that the motion trajectory planned using the proposed method is notably smoother. Compared with the traditional trapezoidal velocity method, it reduces the maximum swing amplitude of the bucket by 40.78% at a modest time cost. In real-time obstacle avoidance scenarios, the approach outperforms emergency-stop strategies, reducing the bucket’s maximum swing amplitude by 30.48%. This work will provide a reference for engineers to optimize the trajectory of large lifting equipment in construction fields such as high arch dams and bridges.