An efficient and accurate method for concrete thermal parameter inversion is essential to guarantee the reliable and prompt thermal analysis results of dams. Traditional inversion methods either suffer from low analysis efficiency or are limited in accuracy. Thus, this paper presents a method for multiple thermal parameter inversion based on an integrated surrogate model (ISM) and the Jaya algorithm. This method replaces finite element analysis with an ISM incorporating three machine learning algorithms, Kriging, support vector regression (SVR), and radial basis function (RBF), to describe the mapping relationship between thermal parameters and structure temperature responses. The input datasets for model training and testing are generated by a uniform design approach. Subsequently, a simple and efficient global optimization algorithm, Jaya, is used to identify the thermal parameters by minimizing the error between calculated and monitored temperatures. The effectiveness and practicality of this method are verified by applying monitored data of two strength grades of concrete in a dam. The verification results indicate that the proposed approach can obtain more accurate inversion results than the above individual models. Compared with these models, the inversion errors using ISM are reduced by 8.45%, 3.93% and 20.85%, respectively for C35 concrete, and by 6.53%, 23.82% and 44.43%, respectively for C40 concrete. Additionally, this approach maintains the powerful computational efficiency of surrogate-based optimization, and compared to the methods that directly invert using swarm intelligence algorithms, the analysis efficiency is improved by about 111.7 times.

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.

Spatial positioning of Valid Temperature-measuring Optical Fiber (VTOF) is of great significance for accurately grasping the real temperature distribution of a concrete dam. However, there is barely an effective way to accurately locate the measuring points of optical fiber in concrete dams. A comprehensive data symmetry analysis technique is presented for determining the optimal spatial positioning of valid temperature-measuring optical fibers (VTOFs) in concrete dams. This approach includes a mathematical model to confirm the center of symmetry in monitoring data, which is required to reconstruct the real spatial temperature distribution. This process can identify VTOF locations and verify the reliability of temperature predictions. Experimental validation was conducted using findings from research on a new mathematical model, site measurements from a concrete dam, and corresponding data from distributed optical fibers. These results were successfully applied to the construction of the Baihetan dam; the concrete temperature from 405 pouring blocks in 5 typical dam monoliths was accurately monitored, and the temperature-measuring points of VTOFs were more than 24711. Meanwhile, the optical fiber temperature measurement and temperature field analysis system of Baihetan dam located in southwest China was developed based on accurately acquired temperature measurement data, which provides data basis and engineering application platform support for temperature evolution and temperature field analysis and precise temperature control management of 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.

Low-temperature concrete pouring on dams causes sudden temperature drops on the surface of the previously poured concrete, thus inducing thermal cracks. In this study, a field test and a numerical calculation were performed on the temperature drop effect caused by low-temperature pouring to prevent thermal cracks in concrete dams. Firstly, a small spatial-scale temperature-gradient monitoring scheme was designed for concrete dams based on the distributed temperature sensing (DTS) system. Secondly, a field test was conducted during the construction period of the Baihetan extra-high arch dam using this scheme, analyzing the temperature response of low-temperature pouring. Finally, a numerical simulation model was established on the temperature field and stress field of the dam, studying the temperature drop effect caused by low-temperature concrete pouring under different construction conditions and its countermeasures. The results indicate that the temperature monitoring scheme proposed in this paper could effectively capture the temperature drop effect at a small-spatial scale caused by low-temperature pouring. This effect, as a negative impact on crack prevention of dam concrete, is effectively weakened using uniform surface-flowing water cooling with a temperature of 22–24°C.