Feedback Design of Temperature Control Measures for Concrete Dams based on Real-Time Temperature Monitoring and Construction Process Simulation

Infiltration and inflow (I/I) of unwanted water in separate urban sewer networks are critical issues for sustainable urban water management. Accurate quantification of unwanted water I/I from individual sources into a sewer system is an essential task for assessing the status of the sewer network and conducting rehabilitation measures. The study aim was to quantify extraneous water I/I into a sanitary sewer network by a temperature-based method, i.e., fiber-optic distributed temperature sensing (DTS), which was applied for the first time in a separate sewer network of a catchment in Trondheim, Norway. The DTS technology is a relatively new technology for sewer monitoring, developed over the past decade. It is based on continual temperature measurement along a fiber-optic cable installed in the sewer network. The feasibility of this method has been tested in both experimental discharges and for the rainfall-derived I/I. The results achieved from the monitoring campaign established the promising applicability of the DTS technique in the quantification analysis. Furthermore, the application of this method in quantifying real-life, rainfall-derived I/I into the sewer system was demonstrated and verified during wet weather conditions. Keywords: distributed temperature sensing (DTS); extraneous water infiltration and inflow (I/I); rainfall-derived infiltration and inflow; sewer system; stormwater management; sustainable urban water management

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“…Imperfections along the glass fibers reflect the laser light. The reflection spot of the laser light can be localized as [8,28]:…”

Section: Distributed Temperature Sensing (Dts) Technologymentioning

confidence: 99%

Quantification Assessment of Extraneous Water Infiltration and Inflow by Analysis of the Thermal Behavior of the Sewer Network

Beheshti

Sægrov

2018

Water

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“…Pei et al [19] combined a response surface model and a genetic algorithm to determine the concrete thermal parameters according to the internal temperature data of a dam, which improved the computational efciency. Zhou et al [20] frst used the dam monitoring temperature and FEM to obtain the datasets, and then used BPNN to establish a mapping model between thermal parameters and concrete temperature. Finally, GA was used to invoke the trained neural network model to obtain the thermal parameters.…”

Section: Introductionmentioning

confidence: 99%

Thermal Parameters Inversion Method for Concrete Dam Based on Optimal Temperature Measuring Point Selecting

Wang

Zhou

et al. 2022

Mathematical Problems in Engineering

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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.

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“…Afterwards, Zhong [12] established a real-time construction progress control model that considered temperature information and quality inspection information during the construction period. Zhou [13] also proposed a simulation model that combined the real-time temperature monitoring data and the actual construction schedule within the temperature field simulation model. Although these studies provide technical support for arch dam construction schedule analysis and management, the current construction simulation model cannot accurately simulate the joint grouting construction time.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

et al. 2020

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Joint grouting simulation is an important aspect of arch dam construction simulation. However, the current construction simulation model simplifies the temperature factors in joint grouting simulation, which leads to the difference between the simulation results and the actual construction schedule. Furthermore, the majority of existing temperature prediction research is based on deterministic point predictions, which cannot quantify the uncertainties of the prediction values. Thus, this study presents a real-time construction simulation method coupling a concrete temperature field interval prediction model to address these problems. First, a real-time construction simulation model is established. Secondly, this paper proposes a concrete temperature interval prediction method based on the hybrid-kernel relevance vector machine (HK-RVM) with the improved grasshopper optimization algorithm (IGOA). The hybrid-kernel method is adopted to ensure the prediction accuracy and generalization ability of the model. Additionally, the improved grasshopper optimization algorithm (IGOA), which utilizes the tent chaotic map and cosine adaptive method to improve the algorithm performance, is developed for the parameter optimization of HK-RVM. Thirdly, concept drift detection based on variable window technology is proposed to update the prediction model. Finally, an arch dam project in China is used as a case study, by which the superiority and applicability of the proposed method are proven.

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Feedback Design of Temperature Control Measures for Concrete Dams based on Real-Time Temperature Monitoring and Construction Process Simulation

Cited by 31 publications

References 10 publications

Quantification Assessment of Extraneous Water Infiltration and Inflow by Analysis of the Thermal Behavior of the Sewer Network

Quantification Assessment of Extraneous Water Infiltration and Inflow by Analysis of the Thermal Behavior of the Sewer Network

Thermal Parameters Inversion Method for Concrete Dam Based on Optimal Temperature Measuring Point Selecting

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

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