Mastering the real temperature distribution of the concrete dam is the basis for solving the problem of temperature control and crack prevention. In this paper, Distributed Temperature Sensing (DTS) technology was applied to temperature monitoring of a high-arch dam under construction in southwest China. In order to obtain a more comprehensive temperature distribution of the dam, optical fiber layout principles for arch dams were studied, and horizontal and vertical optical fiber layout schemes were first proposed according to these principles. The real temperature variation processes of the dam were obtained in real time with a line temperature measurement pattern instead of a point temperature measurement pattern. Additionally, a framework of distributed optical fiber data acquisition and remote transmission was proposed. Interconnection of multiple DTS hosts and remote transmission of temperature data were realized. Then, two-dimensional temperature fields of a typical dam block in different ages and longitudinal profiles of dam blocks with embedded fibers were reconstructed based on large amounts of temperature monitoring data and the Kriging difference algorithm. Temperature field reconstruction results showed that the temperature distribution law of the concrete arch dam was in accordance with the actual situation.

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.

In the concrete-dam-construction process under real-world circumstances, concrete-production schedules can rarely be executed as planned due to the occurrence of various and unpredictable production-system faults, which greatly influence the rapid succession, quality, and quantity of concrete placement. Therefore, identifying these faults beforehand and determining the critical faults are essential for construction management to guarantee harmonious and rapid operations during the dam-construction process. Construction-simulation technology has proven to be an effective approach for predicting the concrete-dam-construction process and the performance of project schedules. However, existing simulation models do not currently consider the adverse influence of concrete-production-system faults. This paper is intended to determine the impact of production-system faults on concrete placement duration, monthly placement strength, and mechanical placement strength. An analysis of the fault-response mecha...