Study On Temperature Monitoring By Distributed Optical Fible And The Intelligent Feedback For Hydraulic Quasi-mass Concrete Structure

For a long time, a common view is that no special temperature control measures are needed for quasi-mass concrete structures since their surface area is large, and they are always contacted with wall rock, which is a good heat elimination condition. Nevertheless, compared to the hydraulic mass concrete, the quasi-mass concrete have the followed characteristics:short semi-mature age, high adiabatic temperature, rapid temperature change, large amount of autogenous shrinkage, small thickness-width ratio, large heat elimination area, intensive restraint. Numerous studies have shown that, these characteristics are more likely to lead to cracks due to excess temperature stress. In this thesis, based on the previous studies, combining with actual projects, temperature monitoring, autogenous deformation monitoring, intelligent inversion analysis method for thermodynamic parameters and intelligent optimization for temperature control measures are deeply studied from theoretical and experimental aspects, and the pattern of "field monitoring-feedback analysis-real behavior analysis-temperature measures control" is proposed to reduce crack risk for hydraulic quasi-mass concrete. The main contains are as follows:(1) The application of temperature monitoring by distributed optical fiber for hydraulic quasi-mass concrete during the construction periodCombining with actual projects, a series of laying process for distributed optical fiber that is suitable for the quasi-mass concrete’s casting environment was explored to guarantee the survival rate and the data’s accuracy, which will provide a new reliable way for concrete’s temperature monitoring. Based on abundant temperature data, the temperature field distribution was analysed for hydraulic quasi-mass concrete maintaining cooling pipe from the perspective of maximum temperature, the vertical and horizontal temperature gradient, the rate of cooling and so on.(2) Calculation model and statistical model of autogenous deformation for hydraulic quasi-mass concrete considering temperature histories.Based on hydration equation of cement-based material and maturity theory, a calculation model and a statistical model of autogenous deformation for hydraulic mass concrete are proposed fully considering the influences of temperature on the thermal dilation coefficient(TDC) and the autogenous deformation at early age. (3) Intelligent inversion analysis method for thermodynamic parameters of quasi-mass concreteThe genetic algorithm-neural network artificial intelligence method is adopted for intelligent inversion analysis for thermodynamic parameters of quasi-mass concrete. Combining with distributed optical fiber temperature monitoring data of specific engineering, the intelligent inversion program is worked out to optimize the multiple thermodynamic parameters.(4) Pipe cooling influence factors of quasi-mass concreteCombining with the distributed optical fiber temperature monitoring data, temperature and stress field simulation under an actual quasi-mass concrete project during the construction are carried out by using the cooling pipe precision finite element program. Through comparative analysis of the laying position of cooling pipe, the initiatial time of pipe cooling and the flow of cooling water, this thesis notes that there are both relating and conflicting relationship among the initiatial time of pipe cooling, the temperature of cooling water and concreting temperature of quasi-mass concrete. Then, from two aspects of high-temperature reason and high-temperature reason, this thesis puts forward that it is reasonable to reduce the maximum temperature and avoid the phenomenon of rapid temperature drop by using the diversified water cooling measures during the construction combining with the monitoring data of key parts. It could provide important reference for the similar pipe cooling project.(5) Intelligent optimization for temperature control measures of quasi-mass concreteRegarding the both relating and conflicting relationship among the temperature control measures of quasi-mass concrete, the genetic algorithm-neural network artificial intelligence method based on uniform design was proposed and the intelligent optimization program was worked out to optimize the multi-factor of temperature control measures. It can overcome the large amount of workload of single factor sensitivity analysis of temperature control measures. Combining with specific engineering, using this intelligent optimization method, the corresponding temperature control measures are selected by the criterion function based on the different security coefficient.