Traditional ventilation methods consume excessive energy but still fail to meet requirements in underground tunnel group construction. Thus, a closed-loop intelligent control system for ventilation-on-demand (VOD) was developed. To address dynamic changes in ventilation load and reduce energy consumption, firstly, the developed system calculates the real-time ventilation load and establishes a ventilation-network-based control mode to represent the ventilation system structure. The deep deterministic policy gradient (DDPG) method was then employed for the closed-loop control ensuring the required air volume in each branch of tunnel groups while minimizing energy consumption. After that, the developed closed-loop intelligent ventilation control system encompasses comprehensive perception, real analysis, real-time control, and continuous optimization. This system treats decision-making, control, and feedback as subsystems that reflect the adaptability between ventilation efficiency, construction progress, and power consumption. Finally, the end-edge-cloud-based software of the system was developed to enable remote control and display on large screens, personal computers (PCs), and mobile applications (Apps) to ensure precise and timely operation. The system was employed in tunnel group under construction at the Xulong Hydropower Station in Southwestern China, and the obtained results validate its advanced closed-loop control based on reinforcement learning (RL) and confirm its feasibility in engineering practice.
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Anchors are typical mass concrete structures found in large bridges, characterized by large structural sizes, complex boundary conditions with irregular shapes, low reinforcement ratios, high crack resistance requirements, and challenges in temperature control and crack prevention. The development of an adaptive, intelligent cooling control method and system is crucial for crack prevention and improving concrete pouring quality.
This paper proposes an intelligent cooling control method for bridge anchorage, including: (1) The basic control principles of heat balance of supply and use, accurate control, and online warning. (2) A fundamental intelligent control strategy involving real thermal field simulation and a temperature-flow coupling control algorithm. The combined influence of temperature and flow is considered when predicting the cooling system parameters. This study uses a hybrid approach involving a long short-term memory neural network (LSTM) and proportional integral derivative (PID) control algorithms to predict the future water flow rate based on the current concrete and cooling system state parameters, facilitating the temperature-to-flow mapping. (3) A "multiple terminal-edge computing-cloud storage" control model is implemented, which incorporates edge computing within the control cabinet, providing localized endpoint services to improve data transmission performance, ensure real-time processing, and reduce latency. Cloud computing uses machine learning to provide instructions for adjusting temperature and flow rates based on the deviations between the actual and target temperature control curves. Furthermore, fault recognition and rapid diagnosis functions are also implemented. Intelligent cooling control equipments and code platforms are developed for realizing online perception, real analysis, feedback control, remote diagnostics, and early warning systems for the cooling process. The system comprises water supply, reversing, control and heat exchange subsystems, and a multiterminal software platform based on WeChat and the web.
This paper adopted simulation, equipment development, and field application methods based on the Longmen Bridge project. Real temperature field simulation calculations were conducted, the temperature distribution during the cooling process was analyzed, and the impact of heat transfer from the upper layer of concrete, as well as the design of cooling pipes, was optimized. Parameters such as water temperature, water flow, concrete temperature, and temperature gradient were analyzed. Furthermore, as part of a long-term temperature monitoring process, the impact of heat transfer from the upper layer of concrete was assessed to reduce the temperature difference between layers. A personalized water-cooling strategy was proposed, and the timing of the water supply was adjusted.
The established temperature-flow coupling control algorithm, model, equipment, and platform achieve real-time monitoring, analysis, control, continuous optimization, and early warning of water-cooling information online and remotely. The study results are successfully applied to the west anchorage of the Longmen Bridge. No temperature cracks are observed on the bridge site, which reduce manpower and water consumption. The results can be used as a design and construction reference for thermal cracking control in similar projects.
Because of favorable wind resource conditions and a lack of land occupation limitations, offshore wind power has been gaining an increasingly important role in the global energy strategy. However, scour is a widespread problem around offshore wind power foundations, resulting in a decrease in foundation bearing capacity, changes in structural natural frequency, and submarine pipeline exposure. As a result, monitoring and early warning of scour are essential.
This study studied the scour process and its dynamic characteristics before proposing a method for identifying the scour initiation in design. For scour monitoring, multibeam sonars, the most often used scour measurement method, have problems of high cost and discontinuous operation, making it impossible to provide on-site scour data in a timely manner. Herein, a method for scour monitoring using structural vibration frequency is proposed. Then, based on ABAQUS, an integrated model of a wind turbine tower foundation was established to study the correlation between the scour depth and the first-order natural frequency, and the feasibility of using the structural vibration frequency to estimate the scour depth. As a result, a scour monitoring method and system based on low-frequency vibration data were developed. The data is acquired in real time by vibration sensors installed in specific parts and processed using a fast Fourier transform after data filtering to obtain the time-domain and frequency-domain characteristics necessary to determine whether the scour is normal.
The numerical simulation results revealed that the first-order frequency of the structure was basically linear with the scour depth and that the frequency decreased by 0.009 3 Hz (3.3%), 0.017 2Hz (6.3%) and 0.027 0 Hz (10.2%) for the scour depths of 3.0 m, 6.0 m and 9.0 m, respectively, compared to the scour-free condition (0.281 2 Hz). The monitoring data from an offshore wind farm in Jiangsu revealed that: (1) The installation orientation and height of the vibration sensors had essentially little effect on the first-order frequency; however, the vibration amplitude decreased as the installation elevation drops. (2) The variations of scour depth and frequency were basically consistent with the numerical results: the scour depths of turbine units #7, #15 and #17 increased from 3.47 m, 5.21 m and 6.11 m in September 2019 to 5.12 m, 5.48 m and 6.95 m in April 2020, while their vibration frequencies decreased from November 2019 to July 2020 by 0.001 3 Hz, 0.001 1 Hz and 0.002 3 Hz, respectively.
Due to the lack of monitoring data, the frequency and scour depth do not fully correspond in time and space. There is an inconsistency between the change in frequency and scour depth of different units, but the monitoring data of all units show that the correlation between the two is clear. As a result, this paper suggests that when the frequency drops by more than 0.010 0 Hz in operation, the system will issue an early warning message prompting the cause of the accident to be investigated. The paper further discussed the future direction of the scour monitoring improvement, and the study results can be used as a reference for similar projects worldwide.
Bridge anchorage core concrete, a typical mass-filling marine concrete structure, faces challenges in temperature change control and crack prevention due to its special shape, continuous casting, and complicated boundary.
Based on the mass-filling concrete of the Guangxi Longmen Bridge anchorage basement (58 606 m3), this paper conducts an online monitoring and analysis of the real thermal field and stress distribution according to the evolution mechanism of the concrete temperature gradient during the pouring period. This work includes developing a temperature gradient digital monitoring system to provide feedback on the deviation from the actual value and provide a basis for timely warning and dynamically adjusted accurate temperature control, proposing the cracking control gradient index as the space and time gradient indices (a dimensionless index), and reconstructing the temperature field to the evolution of the real thermal field base on the temperature measurements in concrete, which is of great importance for the cracking control of the concrete structure.
The main study results are as followed: (1) A major challenge in concrete cracking control was investigated according to complex structural properties, the continuous casting method, high temperature, high humidity, strong wind, and a high salt mist environment. (2) The monitoring data of the temperature gradient digital monitoring system indicated a certain difference in the temperature development in the center concrete and the area near the surface. The temperature in the concrete central area underwent a rapid increase and tended to be stable, stabilised temperature range of 53.60—54.50 ℃, and the temperature increase reached 88.16%—99.34% of the adiabatic temperature increase. The temperature near the concrete surface underwent a rapid increase and a slight decrease, peaking at 52.90 ℃. (3) The threshold values of the space gradient and time gradient indices were defined as -3.00—3.00 ℃/m and 0.002 h-1·m-1, respectively. The temperature gradient index met the threshold requirement, the horizontal and vertical spatial temperature gradients at the stable stage were -0.15—0.14 ℃/m and 0.29—1.08 ℃/m, respectively, and the time-temperature gradient was within 0.002 h-1·m-1. These results indicated that the concrete heat exchange process was performed as small temperature changes in time and space. (4) The temperature field reconstructed from the monitoring data revealed that the real temperature gradient characteristic of the mass-filling concrete and isotherms was dense near the pile foundation at 96 h, then gradually became sparse, and the time-temperature and space gradients gradually became uniform and remained uniform after 144 h. (5) The evolution of the real thermal field, from a nonuniform distribution to a uniform distribution, could be divided into three stages, i.e., thermal accumulation, thermal release, and thermal transfer. The concrete internal stress simulation indicated that the maximum tensile stress occurred at the stress concentration zone along the intersection of the circumferential pile foundation and was substantially affected by environmental temperature change. The maximum tensile stress value was 1 780.0 kPa, and the corresponding safety factor was 1.03, satisfying the design requirements.
A case study shows that the temperature gradient digital monitoring system successfully supports the dynamically adjusted temperature control and effectively controls the cracking risk. These study results can be used as a reference for the cracking control of similar projects.
Diverting flood via a dam gap or diversion tunnel is an economical and efficient method for the construction of a roller-compacted concrete (RCC) dam during the flood season. However, in the tropical climate of Africa, dam-gap diversion has a great influence on the dam temperature and stress field, which increases the risk of surface cracking.
This paper analyzes dam temperature and stress evolution characteristics in high-temperature climatic conditions in tropical areas and develops a method for dam-gap intelligent temperature monitoring and feedback control. Relying on the Nyerere hydropower project, which has the largest installed capacity in East Africa, this paper adopts simulation, equipment development, and field application methods. A three-dimensional finite element model of the Nyerere hydroelectric dam during construction was established. The simulation boundary conditions were determined by the measured dam and river water temperatures. The dam gap concrete temperature and stress field were simulated under water pipe cooling conditions lasting for 0, 7, 14, and 21 d after pouring. After water pipe cooling, in the dam's elevation (EL) 77.0—95.0 m area, the temperature of the overwater surface concrete was not affected remarkably, but the internal temperature of the dam was remarkably reduced. The tensile stress on the overwater surface of the dam gap increased rapidly within a few days after the start of dam-gap diversion. The tensile stress continued to increase gradually and reached a peak at the end of the dam gap diversion. Furthermore, the self-developed intelligent temperature control system 2.0 was used to monitor and control dam body temperature throughout the dam-gap diversion period and to dynamically adjust the cooling strategy.
The main findings were as follows: (1) This article revealed the temperature and stress field evolution characteristics of the dam under different water cooling schemes during the dam-gap diversion stage. A large temperature gradient was generated in the area within 3 m of the overwater surface. The maximum surface temperature stress without water cooling measures reached 2.04 MPa, which exceeded the allowable tensile stress. The risk of cracking could be effectively reduced by reducing the internal temperature of the dam. (2) An intelligent temperature control strategy for hot climate conditions was proposed. It is recommended that the EL 77.0—95.0 m area of the dam was water pipe cooled for at least 7 d and that the temperature at 2 m below the water crossing surface was cooled to < 34.0 ℃ before dam-gap diversion. (3) An intelligent cooling control system 2.0 was developed. This system could intelligently regulate the cooling water temperature and flow supply and change the cooling water flow direction at regular intervals. It could effectively improve the concrete cooling effect, reduce the cooling energy consumption, and cool the dam temperature to the target temperature range before dam-gap diversion. The post-flood inspection detected no temperature cracks.
It is indicated that the combination of temperature control simulation and the intelligent cooling control system 2.0 can effectively solve the temperature cracking problem in dam gaps. The study is of great significance for preventing RCC dam gaps from temperature cracks and can be used as a reference point for similar projects.
This study aims to develop a full participation flat closed-loop (FPFCL) safety management method for offshore wind power (OWP) construction sites. People participation in safety management is improved by giving rewards based on evolutionary game theory. The method avoids management deficiencies due to information loss by reducing redundant management hierarchies and establishing point-to-point communication. The closed-loop mechanism ensures that a safety hazard is timely rectified. Meanwhile, an OWP safety management system (OWPsafety) is developed based on the social media platform (WeChat). The functions of the system include safety hazard report, processing center, and personal center. The software runs on smartphones and allows all stakeholders to participate in safety management, leveraging the advantages of social media in the sharing of knowledge. The benefits of this systematic approach include the elimination of time and space isolation, the interconnection between different construction parties, and the promotion of participation. The proposed method and system were applied to four OWP construction sites. The monthly rectification rate of safety hazards is maintained at more than 91%. Successful on-site tests demonstrated that the method and system can effectively solve the safety management challenges in OWP projects.
The group analytic hierarchy process (AHP) was used with boxplots and Spearman's rank correlation coefficient analyses. The relevant parameters of the hierarchy of decisions were calculated with Python. This multi-criteria decision-making model was then built. Previous investigations have not been reliable and the basic AHP is not always consistent. This Group AHP was then used to compare various vibro-stone column packing methods for treating the deep overburden dam foundation of a hydropower station in southwest China. The results show that the various project participants have different preferred technical routes. Designers and researchers are more inclined to choose innovative technical methods; while owners, constructors and supervisors are more inclined to use mature, reliable schemes. An intelligent feed device and a method were developed, which improve the quantity accuracy and the quality of the vibro-stone column filler for the deep overburden dam foundation.
Concrete temperature control during dam construction (e.g., concrete placement and curing) is important for cracking prevention. In this study, a short-term temperature forecast model for mass concrete cooling control is developed using artificial neural networks (ANN). The development workflow for the forecast model consists of data integration, data preprocessing, model construction, and model application. More than 80 000 monitoring samples are collected by the developed intelligent cooling control system in the Baihetan Arch Dam, which is the largest hydropower project in the world under construction. Machine learning algorithms, including ANN, support vector machines, long short-term memory networks, and decision tree structures, are compared in temperature prediction, and the ANN is determined to be the best for the forecast model. Furthermore, an ANN framework with two hidden layers is determined to forecast concrete temperature at intervals of one day. The root mean square error of the forecast precision is 0.15