CN-122008399-A - Intelligent temperature monitoring, early warning and regulating method and system for mass concrete

CN122008399ACN 122008399 ACN122008399 ACN 122008399ACN-122008399-A

Abstract

The invention discloses a method and a system for intelligent temperature monitoring, early warning and regulation of mass concrete. The method comprises the steps of constructing a long-period memory environment prediction model based on historical climate data, dynamically predicting environmental parameters such as air temperature, humidity and wind speed to obtain an environment prediction data set, constructing a temperature-humidity-chemical-mechanical multi-field coupling analysis model, correcting the model by utilizing monitoring data, comprehensively predicting temperature, humidity and mechanical response states by combining environment prediction results to obtain a large-volume concrete state prediction data set, further constructing an intelligent early warning system based on crack risk level assessment, comprehensively analyzing the prediction results to judge the risk level of concrete, utilizing a PID control system to implement temperature regulation and control, and finally, establishing an integrated visual display platform to dynamically display and manage the whole monitoring, prediction, early warning and regulation processes.

Inventors

MENG JIAN
Dong Lianru
XIE KEWEI
WANG SHENG
JIANG LIE
ZHU PENG
SUN KAI
XUE YUNZHI
ZHANG DUOXIN
WANG QINGYUN

Assignees

中铁碧源水务昆明有限公司
中铁开发投资集团有限公司
同济大学
华北水利水电大学

Dates

Publication Date: 20260512
Application Date: 20251212

Claims (7)

1. The intelligent temperature monitoring, early warning and regulating method for the mass concrete is characterized by comprising the following operation steps of: The method comprises the steps of arranging an internal sensor in concrete at a construction site and arranging an environment sensor outside the concrete, wherein the internal sensor is used for monitoring the internal temperature, humidity, stress and displacement of the concrete, and the external environment sensor is used for monitoring the external environment temperature, humidity and wind speed; Collecting construction site climate environment data and local historical climate environment data, and constructing an environment dynamic prediction model based on a long-short-period memory neural network to obtain a construction environment prediction data set; Utilizing the construction environment data to monitor a data set, combining the data set collected by an internal sensor, and constructing a mass concrete multi-field coupling prediction model based on a physical information neural network algorithm embedded in a concrete thermal-wet-chemical-mechanical multi-field coupling control equation and combined with a limited boundary condition; Predicting the concrete state based on the construction environment monitoring data set through the multi-field coupling prediction model to obtain a prediction result of the internal temperature, humidity, stress and displacement of the mass concrete; analyzing the concrete state prediction result by using an early warning system based on the crack risk level, and outputting the risk level; when the risk level exceeds a preset threshold, a PID control system is started, PID parameters are adaptively adjusted and linked with a heat preservation cooling device under an intelligent decision or manual intervention mode, and the temperature of the concrete is regulated and controlled; and inputting the monitoring result, the prediction result, the early warning information and the temperature control data generated in the operation steps into a visual platform for display, and realizing real-time management of the monitoring, prediction and regulation processes through the visual platform.
2. The method of claim 1, wherein the internal sensors are layered according to different depth positions, each layer of measuring points are uniformly distributed in a plane range, the horizontal distance is not more than 5m, the internal sensors are used for collecting monitoring data (including temperature, humidity, strain and the like) of the internal state of the poured concrete, the external environment sensors are arranged on the periphery of a foundation pit and in a maintenance area, and environmental climate data during construction and maintenance are collected.
3. The method of claim 1, wherein the LSTM environmental prediction model uses a historical meteorological database for feature extraction, an Adam optimizer and a mean square error loss function for training, and correction by a field environmental data set to predict future construction period environmental temperature, humidity, and wind speed.
4. The method of claim 1, wherein the multi-field coupling prediction model is constructed by: Collecting historical climate environment data of the construction site, and establishing a historical meteorological database; constructing a dynamic prediction model based on the LSTM framework to obtain a construction environment prediction data set; based on energy conservation, mass conservation and momentum conservation equations, introducing a concrete elastoplastic damage mechanism, and constructing a temperature-humidity-chemistry-mechanics multi-field coupling analysis model; And combining the environment monitoring data set and the concrete monitoring data, and fitting and optimizing the analysis model by a finite element method and a physical information neural network mixing method to form a final large-volume concrete multi-field coupling prediction model.
5. The method of claim 4, wherein the multi-field coupling prediction model is embedded into a thermal-wet-chemical-force multi-field coupling partial differential equation, and prior parameters such as density, heat conductivity coefficient, specific heat capacity, humidity diffusion coefficient, thermal expansion coefficient, shrinkage coefficient, hydration heat release function, boundary condition function and elastic modulus are introduced, so that model fitting and precision improvement are realized through Bayesian optimization and cross verification.
6. The method of claim 1, wherein the PID control system employs an adaptive PID parameter adjustment mechanism in combination with a thermal insulation and cooling device to achieve intelligent regulation and control and support a manual intervention mode when the risk level exceeds a threshold.
7. The intelligent temperature monitoring, early warning and regulating system for the mass concrete is characterized by comprising an acquisition module, an analysis module, an early warning and regulating module and a visualization module; The acquisition module is used for respectively acquiring a concrete state monitoring data set and environmental climate data through the internal sensor and the external environment sensor; the analysis module is used for constructing an LSTM environment prediction model and a multi-field coupling prediction model and outputting a large-volume concrete state prediction result; The early warning and regulating module is used for analyzing the prediction result based on the crack risk level and regulating and controlling the temperature by utilizing a PID control system when the prediction result exceeds a threshold value; The visual module inputs the data generated in all the operation steps into a visual platform, and the visual platform displays and manages the whole process of monitoring, predicting, early warning and regulating in real time.

Description

Intelligent temperature monitoring, early warning and regulating method and system for mass concrete Technical Field The invention relates to the technical field of concrete construction, in particular to a method and a system for intelligent temperature monitoring, early warning and regulation of mass concrete. Background Along with the increasing requirements of the construction industry on the construction quality and safety of the mass concrete, how to effectively monitor and control the changes of multiple physical fields such as a temperature field, a humidity field, a stress field and the like in the construction process of the mass concrete becomes one of key factors for ensuring the quality and safety of the structure. In the concrete construction process, the temperature change has a remarkable influence on the performance (such as strength, shrinkage and the like), and particularly in the large-volume concrete construction, the temperature stress accumulation easily causes cracks due to the large temperature gradient difference between the inside and the outside of the concrete, so that the durability and the safety of the structure are influenced. Therefore, temperature control and monitoring are critical for mass concrete construction. Traditional large-volume concrete temperature monitoring methods generally rely on manual detection and deployment of a single temperature sensor, and cannot comprehensively reflect changes of physical quantities such as temperature, humidity, stress and the like of the interior and the exterior of concrete in real time. With the development of sensing technology and intelligent technology, various sensors are used to monitor physical quantities such as temperature, humidity, stress, displacement, etc. of concrete, and climate conditions (such as temperature, humidity, wind speed, etc.) of construction sites. However, the existing monitoring system generally cannot realize comprehensive analysis and prediction of various physical quantities, and lacks timely early warning and regulation measures for problems such as cracks caused by temperature changes. In order to improve the safety and quality of mass concrete construction, researchers have tried to realize temperature monitoring, early warning and regulation in the concrete construction process by adopting an intelligent method in recent years. If the internal sensor is used for collecting concrete state data, and the intelligent cloud platform is combined for realizing real-time monitoring and early warning on the conditions of temperature, humidity, stress and the like after the structure is poured. And for example, the concrete state is simulated and analyzed by utilizing finite element technology and the like, and the change condition of each physical field in the structure after pouring is judged, so that preventive measures are guided to be taken in the high-risk area. However, the prior art has certain limitations in practical application. For example, existing methods lack the ability to adapt in real time to complex environmental changes in the construction site, and certain human intervention factors still remain during the optimization of temperature control schemes. Therefore, how to effectively combine the performance monitoring data, the prediction model and the temperature control system to improve the accuracy and the intelligence level of temperature regulation is still a problem to be solved urgently. Disclosure of Invention The invention provides a method for intelligent temperature monitoring, early warning and regulation of mass concrete, which solves the problems in the prior art. The method comprises the following operation steps: an internal sensor is arranged in the concrete at a construction site, and an environment sensor is arranged outside the concrete, wherein the internal sensor is used for monitoring the internal temperature, humidity, stress and displacement of the concrete, and the external environment sensor is used for monitoring the external environment temperature, humidity and wind speed; collecting construction site climate environment data and local historical climate environment data, and constructing an environment dynamic prediction model based on a long-short-term memory neural network (LSTM) to obtain a construction environment prediction data set; Utilizing the construction environment data monitoring dataset, combining the dataset collected by an internal sensor, and constructing a mass concrete multi-field coupling prediction model based on a Physical Information Neural Network (PINN) algorithm embedded in a concrete thermal-wet-chemical-mechanical multi-field coupling control equation and combined with a limited boundary condition; Predicting the concrete state based on the construction environment monitoring data set through the multi-field coupling prediction model to obtain a prediction result of the internal temperature, humidity, stress and displacement of