Crack prevention is a significant issue in the construction process of concrete dams. The vast majority of concrete cracks are related to temperature variations, and hence, temperature control is a primary method used to prevent cracks. This paper presents a new integrated concept, named Feedback Design (FD) of temperature control measures, which incorporates real-time temperature monitoring, temperature field simulation and construction process simulation in a large system to optimize a temperature control scheme for concrete dams. We used a fiber-optic Distributed Temperature Sensing (DTS) system to monitor the temperature variation process of typical dam monoliths in real time. By incorporating a genetic algorithm, neural network algorithm, and finite element theory, we proposed an intelligent inversion method to obtain thermal parameters of concrete dams based on the temperature monitoring data. Meanwhile, we built a simulation model of a concrete dam’s construction process based on a cycling network technique to obtain accurate construction environmental parameters. Finally, we simulated the temperature field of a typical dam monolith based on initial temperature control measures, inversion thermal parameters, and accurate construction environmental parameters. Temperature variance lines of typical points in the model obtained from the finite element method (FEM) accord well with the measured values obtained from the DTS system. We applied the FD approach to the Xiluodu arch dam located in southwest China to forecast the temperature fields and optimize the temperature control scheme of the dam blocks planned for pouring. The FD approach has been proven reliable and efficacious.

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.

Slope excavation quality assessment is essential in water conservancy engineering. This study presents a quality assessment method to estimate the excavation quality of a slope based on point cloud data obtained from a 3D Terrestrial Laser Scanner (TLS). A data processing method, which includes coordinate registration, data partitioning, and noise removal, is proposed to organize raw data from the TLS and ensure accurate and available point cloud data of a slope surface. An excavation quality assessment method with seven indicators, namely, slope ratio, slope toe elevation, back break, volume of back break, remained semi-void rate, unevenness, and offset degree, is proposed to analyze the difference between a slope’s as-design excavation appearance and actual construction appearance. The as-design excavation appearance is expressed by the Building Information Modeling (BIM) model of a slope-excavation face, and it is a reference to assess the actual construction appearance. The slope excavation quality of a water conservancy project in southwest China is evaluated with the proposed method and the traditional method. The results demonstrate that the proposed method can estimate the slope excavation quality effectively, rapidly, and accurately, and can provide theoretical data support for subsequent work.

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.

Concrete thermal parameters in a natural pouring environment are essential inputs for simulating the temperature field of a concrete dam. This paper proposes a two-stage thermal parameters inversion method for a concrete dam based on optimal temperature measuring point selection to improve the accuracy of parameters. Firstly, a selection method of optimal measuring point for thermal parameters inversion is presented and the temperature response sensitivity of measuring points when the parameters disturb is taken as the critical evaluation index. And then, an inversion model is established based on support vector regression (SVR) and particle swarm optimization (PSO). Finally, the proposed method is applied to the thermal parameter inversion of a concrete dam. The results show that the proposed method is effective for improving the inversion accuracy and obtaining accurate parameters. The average error of the inversion results based on the SVR-PSO model is 28.54% lower than that of the genetic algorithm optimization using a back propagation neural network (BPNN-GA). Besides that, the average error of the inversion results based on the optimal measurement points is 35.57% lower than that of the nonoptimized ones.