Hydropower underground engineering encounters significant safety management challenges owing to overlapping construction activities, diverse process stages, and dynamic resource flows. This involves multidisciplinary safety tasks, such as safety hazard identification and rectification, emergency response, and regulatory compliance checks, which require specialized domain knowledge. In this context, safety management knowledge is intricate, such as expert experience, patterns and characteristics, and management codes, and is dispersed across multimodal data formats, including text, tables, and images. Efficient extraction of these multimodal data sources can significantly enhance data utility and support intelligent safety management. However, owing to the diverse nature of data formats, the complexity of the knowledge system, and the various management scenarios, current research struggles with limited knowledge sources, acquisition difficulties, and poor generalization.

This study proposes a method of constructing a multimodal knowledge graph (KG) for safety management in hydropower underground engineering. (1) A large-scale, high-quality, multisource heterogeneous dataset is built from safety hazard identification and rectification records, regulations, and images. (2) Knowledge modeling employs top-down and bottom-up approaches to define entities, relationships, attributes, and events pertinent to safety management in hydropower underground engineering. (3) The entity and relationship information from text data is obtained using a knowledge extraction method that uses a large language model (LLM) tuned with domain knowledge, enriched by specific examples for each entity type to handle small sample sizes. This approach uses demonstrations to provide the model with prior knowledge. (4) Instance segmentation is used to annotate safety hazard images. The entities identified in the images are then converted into vectors. Image and text data are linked based on semantic similarity. Image data are integrated into the textual KG, enabling the transformation from multimodal data to multimodal knowledge. (5) The multimodal KG is stored in Neo4j, an open-source graph database management system. (6) A scenario-specific knowledge acquisition method addresses the specific needs of safety management scenarios, integrating KG with LLMs to enable retrieval-augmented generation and interpretable knowledge reasoning.

(1) This paper collected more than 120 000 safety hazard records, 30 regulatory documents, and 300 000 images of safety hazards. Leveraging these comprehensive data, this paper constructed a large-scale, high-quality, multisource heterogeneous dataset specifically designed for managing safety in hydropower underground engineering projects. (2) Taking a hydropower underground engineering project as an example, the constructed multimodal KG was applied to intelligent recommendations for safety hazard rectification and compliance checks. (3) The workflow for generating intelligent recommendations for safety hazard rectification measures involved the following steps. After users input safety hazard information, the scene-KG was extracted from the multimodal KG and fed into an LLM to generate appropriate rectification measures. (4) Based on the scene-KG, an inference retrieval method extended neighboring nodes and constructed inference-KG for compliance checks. By integrating inference-KG with an LLM, the system retrieved relevant content from regulatory documents based on user input.

The proposed method effectively extracts and applies domain knowledge from multimodal data in the context of safety management in hydropower underground engineering. It also successfully applies domain knowledge for safety management. The results serve as a reference for transitioning infrastructure construction safety management from a data-driven approach to a knowledge-driven approach.

To address the issue of cracking in the lining structures of deep-buried tunnels, this paper proposes the use of basalt fiber-reinforced concrete (BFRC) to improve the load-bearing capacity of lining structures.

Tension and compression tests were conducted on BFRC specimens with varying volume basalt fiber fractions ranging from 0 to 0.5%. The optimal fiber content was determined, and the concrete damage plasticity (CDP) model parameters for plain and fiber-reinforced concrete with the optimal fiber content were validated. Then, numerical simulations were employed to create an integrated bearing model of surrounding rock-initial support and a secondary lining support model. The use of solid and structural elements and a secondary lining support characteristic curve (SCC) for deep-buried tunnels were obtained, revealing the crack propagation characteristics of the concrete. A quantitative analysis was conducted on the effects of the reinforcement ratio, secondary lining thickness, and fiber-reinforced concrete on the normal and ultimate state load-bearing capacity of the secondary lining.

(1) Compared with those of plain concrete (B0), the maximum increases in the axial tensile strength and splitting tensile strength of BFRC with a fiber volume fraction of 0.2% were 12.81% and 14.79%, respectively. Furthermore, a maximum enhancement of 31.68% in the flexural strength of BFRC was noted when the fiber volume fraction was increased to 0.5%. The optimal fiber content was 0.2%. (2) The stress-strain curve of the BFRC could be fitted using peak compressive strength, peak compressive strain, and compressive shape parameters. The compressive shape parameter values for B0 and B0.2 were 6.50 and 3.00, respectively. The tensile stress-strain curve could be fitted using the peak tensile strength, peak tensile strain, and tensile shape parameter, with tensile shape parameter values for B0 and B0.2 being 3.00 and 1.86, respectively. (3) The CDP parameters for plain concrete and fiber-reinforced concrete accurately simulated the peak tensile and compressive strengths as well as the shapes of the tensile and compressive stress-strain curves. For compressive stress-strain curves, the error between numerical simulation and experimental fitting values at 0.50% compressive strain was 2.48% (2.01%) for B0 (B0.2). For tensile stress-strain curves, the error at 0.04% tensile strain was 4.08% (1.68%) for B0 (B0.2). (4) The SCC curve of the secondary lining exhibited rapid linear growth initially, slow growth in the middle, and a nearly horizontal trend in the later stages with increasing displacement. For class Ⅴ surrounding rock, the secondary lining crack width showed slow linear growth in the initial stage and rapid linear growth after reaching approximately 0.10 mm. Higher reinforcement ratios effectively delayed crack propagation in the early stage, although increasing the reinforcement ratio beyond 0.6% or 0.8% was not economically reasonable. (5) Increases in reinforcement ratio and lining thickness resulted in almost linear increases in the 0.30 mm crack load and ultimate state load-bearing capacity. For every 0.1% increase in the reinforcement ratio, 0.30 mm crack load increased by an average of 5.40%. In addition, for every 0.10 m increase in the secondary lining thickness, 0.30 mm crack load increased by an average of 11.18%. Fiber addition considerably enhanced concrete resistance to crack propagation, especially in the early stages, increasing the initial cracking load by 38.64% and the 0.30 mm crack load by 5.54%.

This study provides theoretical and practical guidance for designing deep-buried tunnel lining structures and serves as a reference for applying fiber-reinforced concrete in secondary lining structures of deep-buried tunnels.

Intelligent ventilation on demand is crucial for ensuring environmental safety in underground caving groups and for the high-quality construction and development of hydropower projects. Ventilation systems for large underground caving groups during construction frequently exhibit complex three-dimensional layouts, different air loads across regions, and dynamic demand under varying regulation conditions.

To achieve spatial node extraction, branch correlation decoupling, and stable joint adjustment of complex flow fields, this paper examines the development characteristics of fluids under construction ventilation in extensive spatial structures. It demonstrates the necessity of constructing a graph structure based on the ventilation flow characteristics for analyzing and adjusting ventilation system parameters. The regional modeling theory is discussed, detailing the principles and methods of node extraction for one-dimensional tube bundle fluids (network) and three-dimensional spatial flow field elements (field). Among these, the area where fluid parameter information changes along the main airflow direction employs network node extraction, while the regions with multi-directional complex flow paths utilize the three-dimensional field node extraction method. Virtual branches address the network-field coupling problem, utilizing the nodal pressure approach. This method treats the nodal pressure as the unknown variable and airflow deviation as the assessment criterion. Nodes with known pressure values serve as reference nodes for solving the pressure at all network nodes, and are further assigned to field simulation boundaries. By numerically simulating the three-dimensional spatial flow field, the virtual branch air flow rates are iteratively fed back into the air network calculation for a coupled solution. This paper also introduces the node-property-edge triplet, which effectively reflects the structure, performance, and behavioral characteristics of nodes. Furthermore, to optimize the ventilation coordination efficiency, a hypergraph structure for joint adjustment, with edges as the analysis object, displays the coupling interactions between the ventilation branches and loops. Considering the joint adjustment sensitivity, an optimal resistance control method is proposed, which involves constructing target and response node sets, setting response efficiency constraints, and optimizing to form a ventilation adjustment plan. An intelligent ventilation coordination platform integrates the resistance control model of coupling interactions, including modules for network design, ventilation design, field-network integration, loop generation, and optimization analysis. Within this framework, the network design module is dedicated to reconstructing the physical model of the ventilation system, while the ventilation design and field-network integration modules are used to assign basic fluid characteristic parameters of ventilation to the established model. The loop generation and optimization analysis modules are employed for solving the overall wind network parameters, including air volume, air pressure, and wind resistance.

The field-network coupling method using nodal pressure eliminated the need for loop identification and effectively addressed the interdependent coupling between network nodes and flow field boundaries. The intelligent ventilation coordination platform was integrated with online environmental monitoring devices to automatically gather critical ventilation environment parameters, thereby enabling real-time calculations of the ventilation system based on environmental monitoring data and providing 3D visualization and early warning capabilities.

The ventilation design parameters of an engineering project are used to implement targeted air volume control deployment. The integrated control system exhibits high responsiveness. On the premise that the air volume of each unit meets the threshold requirements, the air volume adjustment efficiency of the target unit and the overall stability of the air distribution network can always fulfill the specified requirements. The results indicate a timely and stable system response and can provide a reference for similar projects.

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.