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Compaction quality plays a vital role in the long-term performance of highways, thereby making quality control (QC) of compaction an essential aspect in highway projects. The prevalent approach regarding QC of compaction primarily depends on manual manipulation of compaction parameters during the construction process and sporadic testing after completion. However, the former method is significantly affected by human factors, whereas the latter entails destructive detection and low efficiency; the latter method also fails to provide a comprehensive representation of the state of compaction across the entire work area of highway projects. Consequently, to achieve QC of compaction, this study used roller-integrated compaction monitoring (RICM) technology combined with the real-time kinematic–Beidou navigation satellite system (RTK–BDS). Initially, the compaction meter value (CMV) and compaction power per unit volume (E) were chosen as the real-time monitoring indexes for assessing the quality of compaction. Subsequently, a case study on the Hengyong Highway Project in China was conducted. The findings revealed that CMV exhibited a stronger correlation with compactness, resulting in smaller data dispersion and higher data stability. Thus, this study chose CMV as the characterization index of the compaction quality of high and low liquid limit (ωL) silt subgrades and constructed a fast compaction quality assessment method based on CMV and the spline interpolation method based on Green’s function to obtain estimates for compaction quality over the entire work area, enabling QC of silt subgrades. The results showed that the proposed method facilitated a swift and uninterrupted evaluation of the compaction quality over the entire construction area. Thus, the proposed method can facilitate the effective prevention of compaction quality defects, thus enhancing the overall quality of highways.

Many parameters and complex boundaries are involved in the spatial arrangement of an underground powerhouse in a hydropower station, necessitating referencing many relevant cases and specifications. However, in practical applications, retrieving such cases or specifications is difficult, and there is a lack of knowledge regarding cascading logic among design parameters. To address this issue, this study proposes a novel methodology for constructing a targeted knowledge graph, in this case, a knowledge graph for building information modeling (BIM) for underground powerhouses in hydropower plants. Subsequently, based on this knowledge graph, this study develops a question-and-answer (Q&A) system to facilitate subsequent applications. First, the ontology skeleton of the spatial arrangement design of an underground powerhouse in a hydropower station, that represents the knowledge organization structure of the knowledge graph, is constructed by carefully analyzing the requirements for intelligent modeling of underground powerhouses. A large volume of unstructured data is identified based on the optical character recognition (OCR) technology; the collected data are divided into words to extract correlation knowledge using THU Lexical Analyzer for Chinese (THULAC). Subsequently, the knowledge triad of the spatial arrangement of the underground powerhouse is extracted based on ChatGPT and stored in Neo4j, a knowledge base, to build a knowledge graph. Finally, the knowledge graph is employed to realize the query of knowledge and parameter recommendation to assist the digital intelligent design of the spatial arrangement of an underground powerhouse in a pumped storage hydropower station.

Due to the complex and dynamic nature of construction environments, safety accidents occurring in these environments pose a grave threat to life and property. Therefore, it is essential for safety managers in construction, supervisory, and related units to adopt a rigorous and systematic methodology for assessing the risks associated with construction safety accidents. This will enable managers to comprehend the likelihood of accidents, subsequently enabling them to implement preemptive and control measures to reduce the probability of such incidents. Drawing on the accident causation theory, this study utilized web crawler technology to collect construction accident reports, subsequently employing text mining (TM) techniques to identify the accident causal factors specified in 166 accident reports. Subsequently, 33 key features were extracted from the accident causal factors, and correlation rule mining was used to analyze the correlations between the causal factors. Successively, a Bayesian network (BN)-based risk assessment model was constructed for construction safety accidents. Finally, through reverse reasoning, this study identified the probable paths of construction safety accidents and the sensitive factors that trigger such accidents. The results showed that management factors (MFs) are the primary drivers of accidents, highlighting the importance of focusing on preventive and control countermeasures for factors characterized with high severity and sensitivity.

Grand challenges such as achieving sustainability goals and managing aging infrastructure are creating an unprecedented demand for deconstruction. However, deconstructing aging infrastructure is inherently risky, repetitive, and costly, necessitating effective project planning. Virtual reality (VR) technology offers the potential for planners to improve deconstruction project risk, time, safety, and cost. We propose that planners leverage VR early in the planning phase to compare alternative feasible tools and processes. Planners can create low-fidelity models of various deconstruction process alternatives and collect metrics on their suitability with VR-capable three-dimensional (3D) game engines. We present and formalize a methodology for conducting comparisons of candidate deconstruction processes by modeling candidates in VR, conducting trials, and collecting analytics data from the VR engine. We present a case study that demonstrates our approach to a cutting and waste packing process for nuclear power plant (NPP) decommissioning. As a novel contribution in this paper, we show that VR simulations can efficiently produce detailed insights useful for critically analyzing and comparing deconstruction process alternatives.

A hydropower project has four significant stages: artificialization, mechanization, automation, and digitization. Currently, it is in the intelligent stage and is advancing toward smartization. This study first briefly introduces the development process of intelligent construction and smart operation of the Lancang River Hydropower Project, and comprehensively summarizes the research, development, and practice in intelligent construction of the project and smart operation management of the reservoir dam. Then, the main functions and effects of digital construction of high earth–rock dams and intelligent construction of high roller compacted concrete (RCC) dams were elaborated. Special technologies and module functions such as intelligent diagnosis of dam operation safety, dynamic control of underground engineering, early warning and prevention of slope disasters, and online monitoring of the water environment of deep and large reservoirs were introduced. Finally, the future development trends of smart hydropower were discussed. The research can be used as a reference in the intelligent construction and smart operation of similar projects.

This paper investigates the hydrodynamics of a bent elastic tube with an instantaneously deformed wall using an arbitrary Lagrangian–Eulerian (ALE) finite element method. The study reveals that the tube deforms continuously with the fluid’s progression, exhibiting diverse deformation patterns. The flow patterns do not adhere to Poiseuille’s profile, with higher velocities detected on the inner side of the bent region during deformation. The results deepen insights into hydrodynamics within bent elastic tubes and bring significance for the design of curved pipelines.

In recent times, due to the rapid infrastructure development worldwide, the use of concrete has greatly increased. However, concrete structures face various risks such as dynamic forces from winds or earthquakes and environmental impacts such as corrosion. These challenges are more significant because of the saturation of infrastructure projects worldwide. To tackle these issues and ensure the long-term strength of structures, the field of structural health monitoring (SHM) has gained significance. One of the most promising technologies for SHM is smart aggregates (SAs). These are special materials that are embedded in concrete and serve three main purposes: monitoring early-age concrete strength, detecting impacts of various factors, and continuously monitoring the health of structures. SAs have several advantages regarding SHM, including being able to detect a wide frequency range, quick response times, cost-effective, and easy to manufacture and process. Because of these benefits, they are becoming widely used for SHM. In recent years, there have been significant improvements in SA technology. This article provides a comprehensive overview of the use of SAs for SHM. It also discusses the advances in SAs in the last 20 years regarding three main areas: technology development, measurement range, and wireless capabilities. Additionally, this article briefly discusses recent real-world uses of SAs for SHM. Finally, this article outlines potential future trends in the development of SA technology. It emphasizes the need for more research to better understand how these materials perform and how effective they are in different situations. This research is crucial for further advancements in ensuring the durability and safety of structures.

The vibroflotation construction is a significant measure for foundation improvement in hydropower engineering. Currently, vibro-stone columns are still constructed manually. The construction process relies mainly on the experience and responsibility of workers, which has the shortcomings of low precision in pile formation control and low degree of automation, leading to problems such as uneven pile formation, high labor intensity, and poor pile quality. This study proposes an intelligent vibroflotation control model (iVCM) based on the analysis of the pile formation mechanism and former experiences. Intelligent control of vibroflotation construction is realized through intelligent control of the vibrator current, rope pull, filler amount, and vibrator depth. A crane-type intelligent vibroflotation control system was developed to realize one-button intelligent control of pile formation. Its application in Yingliangbao Hydropower Station shows that the intelligent vibroflotation control system can regulate the construction operation of the pile formation process, improve the vibroflotation quality, reduce labor intensity, and have prospects for applicability in similar projects.

With advancements in digital technology, the field of architectural design has increasingly embraced data and algorithms to enhance design efficiency and quality. Recent advancements in text-to-image (T2I) generation models have enabled the creation of images that correspond to textual descriptions. However, textual descriptions struggle to capture essential style characteristics in style images. In this study, we proposed a method for architectural facade design based on the stable diffusion model (SDM) that combined stylistic images or keywords as input with the structural conditions of content images to generate images with both stylistic and architectural features. By employing the constrastive language-image pre-training (CLIP) image encoder to convert the style image into its initial image embedding and feature extraction from multilayer cross-attention and training optimization to obtain a pretrained image embedding, the proposed method extracts stylistic features from style images and converts them into corresponding embeddings. This process enables the generated images to embody stylistic features and artistic semantic information. Furthermore, the T2I adapter model is employed to use the architectural structure of content images as conditional guidance, thereby ensuring that the generated images exhibit the corresponding structural features. By leveraging these two aspects, the proposed method can decorate architecture with stylistic features from stylistic images while preserving the architectural structure features of content images, resulting in images that reflect the content images after style transformation. Our method is mainly used in architectural design applications. It was capable of generating facade images from flat design drawings, three-dimensional (3D) architectural models, and hand-drawn sketches and has achieved commendable results.

A major engineering challenge in construction projects lies in the stability assessment of rocky slopes with faults. The left bank slope of the Jinchuan Hydropower Station in the outlet field is notably influenced by the steeply inclined faults and rock joints, resulting in highly complex geological conditions and raising critical concerns regarding slope stability throughout construction and operation. The evolution mechanism of rock mass from fracture to deformation during slope excavation and unloading is determined using microseismic (MS) monitoring and numerical simulation. Results show that the MS activity can effectively reveal the influence of slope construction disturbance on geological structure damage and structural plane activation. Specifically, the g7 fault is a crucial factor influencing the failure mode of the left bank slope of the Jinchuan Hydropower Station in the outlet field. The comparison of monitoring data, numerical simulation, and in-situ failure demonstrates consistency between the deformation area of rock mass and the damage area defined by MS monitoring, confirming the accuracy of MS monitoring and numerical simulation results. Overall, the results can serve as a basis for the later-stage construction of the dam foundation slope at the Jinchuan Hydropower Station and provide guidance for the stability assessment of similar slope engineering projects.

Construction robots have garnered attention with the rapid development of construction automation. Such tiling robots can effectively improve the construction quality and efficiency. To enhance the quality of robotic tile laying, a control method based on position servo and position impedance was proposed herein. To improve the full filling rate, the robot improved the full reduction rate of tile laying using the effective vibration from an eccentric vibrator. Experimental results showed that the proposed method could effectively improve the tile laying quality. The results of this study will promote the application of robotic tile laying to engineering and provide a reference for the improvement in the quality of robotics in construction.

In this study, full-scale fire experiments and numerical simulations of the underground powerhouse of a hydropower station during the construction stage were performed to investigate the temperature distribution and smoke propagation. The characteristic of inverse ambient temperature in the vertical direction was discovered, resulting in the vertical movement of smoke differing from that in a uniform temperature environment. The maximum temperature increase appeared at non-highest points. Smoke characteristic parameters such as smoke settlement height and temperature increase were discussed at different heat release rates (HRRs) of fire sources to determine the fire risk distribution of the powerhouse. Two ventilation modes were proposed, and their smoke control effects were compared. The optimal ventilation mode and volume in different fire scenarios were proposed. The findings offer scenario and data support for fire smoke control and emergency plan design in powerhouses of hydropower stations.

Understanding the advantages of combining traditional construction methods and 3D concrete printing is the gateway to the general structural applicability of three-dimensional concrete printing (3DCP) technology. To further this objective, in this study, 3D-printed concrete is used as permanent formwork for the manufacture of reinforced concrete beams. Different treatment methods for the interfaces between the printed formwork and the cast core concrete, including embedding of shear connectors, entraining of micro-cables, and inclusion of ribs in the 3D printing of the formwork, are explored to improve the integral mechanical capacities of so-called 3D-printed and cast-in-place composite beams. Specifically, flexural behaviors are studied experimentally through observation of damage and failure processes via visualization with the digital image correlation (DIC) method. The mesoscale architecture at the interface between 3D-printed permanent formwork and cast-in-place concrete is investigated through computed tomography (CT) scanning to infer the cooperative bonding mechanisms of different bonding treatment methods. The stress-transfer mechanism is thus elucidated. The applicability of 3D-printed concrete as permanent formwork is validated in view of a 14.3% increase in the bending capacity from a composite beam with bottom ribs compared to that of a completely cast counterpart. From the experiments, incorporation of shear connectors contributes most to the bonding performance of the formwork–concrete interfaces. Meanwhile, interfacial bonding can be enhanced by increasing the roughness of the 3D-printed formwork, interlocking of the formwork with the inner aggregated concrete, or improving the consistency of the elastic and shear moduli of the formwork and cast concrete. In particular, the appropriate thickness of both the formwork and the aggregated concrete as a cover to yield the optimal integrated bending capacity of composite beams is derived from the current study.

Construction safety accidents have become increasingly frequent in recent years, leading to numerous casualties and substantial property losses. These incidents are often attributed to inadequate supervision on construction sites and workers’ low safety awareness. Traditional manual management methods, which are labor-intensive and resource-consuming, are no longer effective. Therefore, this study proposes a novel single-stage model based on YOLOv8s, designed for two primary purposes: detecting workers’ personal protective equipment and monitoring and recognizing when workers enter hazardous areas. The model provides real-time feedback on detection results to reduce the incidence of construction accidents. Additionally, a brief design for distance calculation was introduced. The model was trained for 200 iterations on a Roboflow dataset comprising 103,500 annotated images. Experimental results showed that YOLOv8s outperformed YOLOv8n, YOLOv5s, and YOLOv5n in detection performance, achieving a mean average precision with the intersection over union (IoU) threshold set to 50% (mAP50) of 84.0%, precision of 85.0%, and recall of 60.5% across 9 detection classes. By leveraging artificial intelligence technology, this study aims to offer an effective method for enhancing construction site safety, which can be further improved with additional images and a more robust network architecture.

Addressing the challenge of breaking rocks deep within the earth requires innovative solutions, and the pulsed laser technology has emerged as a promising noncontact alternative for rock breaking. We developed an automated experimental platform with robotic arms and a rock mechanics testing device to quantitatively study the impact of laser technology on rock breaking. Our investigation delved into the damage caused to rocks by different types of lasers, focusing on understanding their underlying damage mechanisms. Pulsed lasers, excluding thermal effects, reduced the red sandstone strength by 68.07%, with a decrease in peak tensile force by 30–33 N per 1000 pulses. Furthermore, we conducted detailed quantitative analyses to understand how laser power (P), irradiation duration, and output energy affeted rock damage. Increasing laser power or irradiation duration enhanced rock damage since this increased the laser output energy. For every 100.00 W power increase, the peak force decreased by 51.00–88.00 N, and for every 1000.00 ms duration increase, the peak force decreased by 34.00–42.00 N. Maintaining constant output energy while changing the laser power revealed a quadratic nonlinearity in peak force, aiding in determining optimal laser parameters. This study highlights the potential of laser technology for effective rock fracturing during deep earth exploration. The established experimental platform improves the accuracy and reliability of laser-induced rock breaking, offering insights for future advancements in this field.

Under environmental influence, newly poured concrete experiences excessive evaporation of surface water, which degrades concrete performance. Therefore, the water change process inside the concrete at construction sites needs to be monitored accurately. However, existing technologies cannot fully meet this requirement. In this study, the theoretical relationship between water and dielectric constant changes inside concrete was analyzed on the basis of the dielectric constant characteristics of various components inside concrete. On the basis of the principle of standing wave ratio, a real-time and in situ electrical measurement technology for water content was proposed. The results show quadratic polynomial correlation between the measured voltage of the sensor and the water content of the concrete, and the experimental verification results show a correlation coefficient greater than 0.95 between the measured values and the theoretical relationship. The results of the application of the method in a water conservancy project site show that it has good value for in situ applications and can accurately and automatically monitor the distribution of the water content of concrete in real time.

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.

Ceiling beams at the top of tunnels are more common in actual projects. Under the influence of thermal buoyancy, the ceiling structure significantly affects the diffusion characteristics of fire smoke within the tunnel. This study investigated the influence of ceiling structural characteristics (beam height (hB) and beam spacing (dB)) on tunnel longitudinal ventilation through numerical simulation. The results show that the spacing between tunnel ceiling beams has negligible impact on the critical velocity (V), and the determination of the critical velocity is primarily correlated with the height of the ceiling beams. Moreover, it established a dimensionless critical velocity (Vc) model for the tunnels with multiple beams in the ceiling, and this model is suitable for predicting the critical longitudinal velocity of tunnels with ceiling beams whose dimensionless beam height is less than 0.25. When the dimensionless beam height exceeds 0.25, the predictive values of this model are excessively high. This study broadens the application scope of fire smoke control models, which can offer technical support for the design of smoke prevention and exhaust systems in tunnels with similar structures.

During the excavation of tunnels in mountainous areas, groundwater may be lost, which affects the surface plants and ecology. In this article, taking Hengwu Tunnel in China as an example, based on the soil–plant–atmosphere continuum (SPAC) model, the relevant parameters were obtained by field test first, and then from the perspective of soil water matrix potential (SWMP) and soil water migration (SWM), the effect of groundwater level decline induced by mountain tunnel excavation on plant growth was studied, and the calculation method of ecological water level was put forward. The results show the following: (1) The wilting of plant roots is a dynamic process of gradual expansion from the middle of the root to both ends, and the response of SWMP in the root region to changes in atmosphere and groundwater level is lagging and non-uniform; (2) SWMP can be used to predict the degree of wilting of plant roots, while the final distribution and value of SWMP are only related to the position of the groundwater level, but not related to the decline rate of the groundwater level; (3) groundwater level and rainfall (P) will affect the value and proportion of each flux in the SPAC model, in which the relative transpiration ratio can be used to evaluate the growth of the plant and calculate the ecological water level of the plant.

In this study, we investigated the mechanism of clayshock during the construction of a new shield tunnel that passes through an existing shield tunnel. The experimental findings demonstrated a significant reduction of approximately 12% in the friction between the shield machine and the clay and silt layers. After the clayshock concentration exceeds 400 kg/m³, it may successfully impede the movement of the slurry, completely fill the space between the formation and the shield, and minimize the surface settling. Furthermore, Plackett–Burman testing was used to assess the sensitivity of the primary shield characteristics that affected settlement. The findings indicated that settlement was greatly influenced by the earth pressure, grouting quantity, and clayshock efficiency. A full factor analysis revealed an intricate link between the shield properties and settlement. If the volume of grouting was less than 6.5 m³, it was very difficult to regulate the settling value within −1.0 mm. We utilized the following neural network models: gated recurrent unit (GRU) network, long short-term memory network (LSTM), and Elman recurrent neural network (ELMAN). The training input data consisted of geometric, geological, and shield parameters, and the prediction output data served as the settlement value. Researchers will be able to generate accurate and reliable predictive outcomes by improving the model training process.

The orthotropic steel bridge deck (OSBD) has been widely used in recent decades, benefiting from its advantages of lightweight and easy assembly. However, the longitudinal and transversal stiffeners of OSBDs are connected to the top flange plate through dense welds, which will easily introduce fatigue cracks. Hence, the composite bridge deck (CBD) system was proposed, adding a concrete layer over a steel plate to increase the sectional stiffness of OSBDs and reduce the fatigue stress amplitude. Furthermore, some new materials with extraordinary properties such as ultra-high-performance concrete (UHPC) and engineering cementitious composites (ECC) were used to replace the normal concrete, to improve the bearing capacity and crack resistance of CBDs. In this review, four kinds of bridge deck systems with different structural types including OSBD, steel–concrete, steel–UHPC, and steel–ECC CBDs were discussed. The flexural performance of four systems under sagging and hogging moments was reviewed, and close attention was paid to the crack resistance of the CBD system. In addition, the shear connection used in the CBDs was concentrated, and the shear behavior of some connectors including studs, perforated rib (PBL), and modified clothoid (MCL) shape shear connectors was investigated. The CBD structure assembled by duplicate profile steel parts proposed recently was introduced and discussed.

Due to the swift progress of subterranean urban transportation, various branching structures emerged one after another. When it brought significant convenience to people’s travel, the fire hazard associated with it could not be overlooked. This paper analyzed the impact of the T-shaped bifurcated structure on the temperature characteristics of fire smoke through fire dynamics simulator (FDS) numerical simulation. The findings revealed that the temperature decrease trend of each section below the ceiling in a T-shaped bifurcated tunnel could be accurately described by a double exponential model. Notably, the attenuation coefficient of each section basically remained unchanged under natural ventilation. However, the coefficient varied distinctly and directly related to the dimensionless ventilation velocities (V') in each section under longitudinal ventilation. The maximum fire smoke temperature below the tunnel ceiling of the T-shaped bifurcated tunnel was higher than that below the single tunnel ceiling at lower ventilation velocities (V' ≤ 0.19), while lower than that below the single tunnel ceiling at higher ventilation velocities (V' > 0.19). On the basis of the numerical simulation data, a predictive model to estimate the maximum fire smoke temperature rise below the tunnel ceiling of a T-shaped bifurcated tunnel was proposed.

Subway tunnel flooding is one of the typical scenarios in urban waterlogging. When the emergency decision-making is untimely and inaccurate, it can lead to serious casualties. However, it is expected that through some responsive monitoring methods, an intelligent system can be constructed to achieve the real-time perception of the underground flood situation and to predict the process and intensity of the disaster. This study is based on a scale model experiment of flooding in straight subway tunnels. Firstly, it classified the basic patterns of the flood process. Some fitting formulas were respectively established for water depth and kinematic parameters. A calculation method for quickly predicting the flood inundation process was constructed. Secondly, this paper proposed a mode for the monitoring and early warning system. Based on the flood patterns and characteristic variables in subway tunnel flooding, the system can quickly determine the flow rate and entry position of flood and deduce the flood trend in the future at a relatively low cost. The system workflow was divided into three phases, and the mechanism for each phase of the system was sorted out. It could help to provide scientific and reliable information for emergency decision-making and gain time for the emergency response.

Smoke control technology is crucial in the risk management of fire in underground confined spaces. In this study, the integrated foam smoke reduction device (FSRD) of “foaming–spraying” is proposed in response to the challenging problem of smoke control of fire in underground confined spaces. The overall structure of FSRD device is designed and optimized through the numerical simulation of the internal flow field and experimental research. The optimal basic parameters are as follows: area ratio (Rm) of 20.25, pitch-to-diameter ratio (Rtd) of 1/3 or 5/9, and throat diameter (d3) of 18 mm . Additionally, the throat–nozzle distance (Lt) is 12 mm, and the operating pressure (p0) is 3.0 MPa. Finally, the smoke reduction experiment verified that the FSRD designed in this study had a better effect of elimination than fine water mist and cylindrical foam jet, with the elimination efficiency reaching 36.73%. This study provides a new device for smoke reduction in controlling fires in underground confined spaces.

The construction of shield tunneling close to risk sources in urban has been a commonly encountered problem in recent years. The establishment of the analytical model used to calculate the immediate settlement of existing structures has been a major focus in approaching construction issues. It is obvious that after shield construction, the settlement will inevitably continue to increase for a long period. In this study, a novel model applied to calculate the long-term settlement considering the influence of soil consolidation was proposed. Based on this proposed analysis model, combined with building information modeling (BIM), geographic information system (GIS), city information model (CIM), geoscience modeling (Geo Model), and internet of things (IoT), a three-dimensional (3D) intelligent management and control platform for shield tunneling construction close to major risk sources was developed and introduced. In this platform, the analysis model was embedded to calculate the long-term settlement, and the risk data were determined by comparing calculated results, monitoring results, and control standard value. The intelligent platform adopts the analytical method and computer technology to solve the problem of approaching construction, such as management of construction information, pre-warning, and control of construction risk, and it has been successfully applied in Beijing shield tunneling projects. This platform can also provide the pre-warning function and economic value for similar construction projects.

Gomal Zam Roller Compacted Concrete (RCC) Arch-Gravity Dam is up to now the only arch-type RCC dam in Pakistan. In the selection of dam axis and type, the local topographical and geological conditions were intelligently used to create the largest reservoir with the minimum dam volume. During the construction, several innovative technologies were applied. This paper highlights the notable structural features and innovative construction technologies.

In the process of modernization with Chinese characteristics, the dam sector of China will embrace a new development climax. A new layout of the national water network will be formed by the combination of major natural river systems and water infrastructures including water division channels, dams and reservoirs, hydropower stations, river dikes, flood retention areas, etc. The massive investment demand released in the construction of many planned/ongoing projects will drive the reform of the supply side. Many new requirements for modern dam construction and operation will be imposed following the national’s goal of high-quality development, and new challenges will arise from aquatic ecosystem protection and restoration. These strategic needs form the basis for new research and development (R&D) missions of China’s hydraulic-hydropower industry.

Road damage detection plays an important role in ensuring road safety and improving traffic flow. The dramatic progress of artificial intelligence (AI) technology offers new opportunities for this field. In this paper, we introduce lightweight attention ghost-you only look once (LAG-YOLO), an efficient deep-learning network for road damage detection. LAG-YOLO optimizes the network structure of YOLO, making it more suitable for real-time processing and lightweight deployment while ensuring high accuracy. In addition, a novel module called attention ghost is designed to reduce the model parameters and improve the model performance by the simple attention module (SimAM). LAG-YOLO achieves an impressive parameter reduction to 4.19 million, delivering remarkable mean average precision (mAP) scores of 45.80% on the Hualu dataset and 52.35% on the RDD2020 dataset. In summary, the proposed network performs satisfactorily on extensive road damage datasets with fewer parameters, making it more suitable to be deployed in practice.

This paper delves into the optimization of traffic safety facilities within highway tunnels by leveraging the inherent visual perception characteristics of drivers. Grounded in the context of Zhejiang Province’s highway construction, the study draws from practical insights provided by highway tunnel projects within the region. Through a meticulous blend of on-site investigations and empirical experimentation, the research assesses the effectiveness of existing safety installations in highway tunnels. The analysis extends to the exploration of optimizing the design and placement of traffic safety facilities, informed by the distinct visual perception tendencies exhibited by drivers. By amalgamating the insights derived from driver perception and real-world highway tunnel dynamics, the paper proposes a refined and contextually attuned approach to enhancing traffic safety measures. This study not only contributes to the field of transportation engineering but also holds the potential to significantly enhance the overall safety of highway tunnel environments.

This paper examines the stability of rock slopes along the broad gauge (BG) line near Darekasa (approximately 1.0 km from Darekasa Station towards the western side). Unsafe slopes and rockfalls can hinder train travel, causing commuters difficulties. A field survey and lab experiments determined the rock slope’s stabilizing factors. Kinematic analysis and finite element modeling evaluated slope stability and design. On-site joint orientations were estimated with a Brunton compass (Nautical Mart Inc., Roorkee, India). Stereonet plots show wedge and planar failure patterns. The RS2 software was used to generate a finite element model for critical slope sections utilizing the combined continuum interface method and to determine critical shear strength reduction factors (SSRFs) with a two-dimensional plain strain method. The stabilization of the subject area was evaluated based on these findings. The purpose of rockfall protection is to prevent the fall of any individual blocks caused by the creation of local wedges. During numerical calculations for the global stability of a slope, these types of failures are not detectable. Along the stretch, this scenario demands drapery/rockfall netting. To preserve the slope against instability and rockfall, corrective measures consisting of reinforced double-twisted hexagonal mesh, rhomboidal cable net, and self-drilling anchors were implemented.

The built environment is subject to various defects as it ages. A well-maintained built environment depends on surveying activities to inspect, document, and rehabilitate the defects that occurred. The advancement of digital technologies paves the pathway towards (1) comprehensive defect inspection by systematic mapping, (2) their consistent documentation by digital modeling, and (3) timely retrofitting by proactive management. However, the three steps of defect mapping, modeling, and management (D3M) remain largely fragmented and have yet to be synergized. Exploiting the pivotal role of building information modeling (BIM) in built asset management, this paper puts forward a cohesive framework for integrated D3M. It leverages the rich geometric-semantic information in BIM to assist defect mapping and enriches the BIM by industry foundation classes (IFCs)-represented defect information. The defect-enriched BIM facilitates defect management in a data-driven manner. The framework was applied in multiple real-life infrastructure and civil works projects. It demonstrates how the BIM-based D3M framework can enhance the maintenance of those that have been built, and ultimately contribute to a safe and sustainable built environment. Future studies are called for to substantiate each of the 3Ms by leveraging BIM as both an enabler and a beneficiary.

Deformable support systems are a new support technology for preventing geo-hazards such as large deformations of engineering rock masses, rock bursts, and roof falling. An intelligent terminal structure (ITS) is a device that consists of a memory tooth and a yield ring. It is assembled at the end of a traditional cable bolt and has the dual functions of adapting to the large deformation of rock masses and sensing the deformation of surrounding rock masses. This study focuses on adapting support systems to the deformation of rock masses dilation of tunnels and other underground engineering projects and monitoring the support systems. The design principle of the ITS of a cable bolt and the technical method for the perception of the deformation of a rock mass are proposed. The results determined that the CB-T6.0-R4.5 design parameters of the ITS of a cable bolt for a deformation support system of surrounding rock masses. This is beneficial for accurately recording the stress and deformation behavior of rock masses through the deformation of the ITS of a cable bolt. Therefore, the cable bolt with an ITS has important value for applications in deformable support technology and safety monitoring of engineering rock mass reinforcement.

Cemented material dam (CMD) can fully utilize local sand, gravel, and rock materials for dam construction. It has many advantages such as flood overtopping without failure, environmental friendliness, economic savings, rapid construction, and so on. It has been rapidly popularised and applied to domestic and foreign projects since it was put forward by the first author. This paper illustrates the reasons for the proposal of CMDs, puts forward the stress–strain model of the cemented mixture and the intelligent system of production quality control, presents the model test results for CMD against flood overtopping, and introduces the practice of dam construction with low-strength soft rock and weathered materials.

The Baise Hydropower Project (hereinafter referred to as “Project”) is the second tier of the comprehensive exploration plan for the Yujiang River of the Xijiang River system in the Pearl River Basin in China. The construction was completed in December 2006. First, the basic facts, layout, and essential structures of the Project were introduced in the article. Second, the primary technological advancements were discussed, including (1) changing layout to accommodate complex topographical and geological conditions, (2) site-specific engineering considerations for the development and construction of high roller compacted concrete (RCC) dams, (3) extensive utilization of diabase for artificial aggregates, and (4) enhanced procedures for expedited construction and quality assurance. Third, intelligent operation and administration used in the Project were described. Finally, the Project’s accomplishments were outlined, followed by project images.

Intelligent drilling has obtained a lot of attention in mining engineering, oil and gas production, and tunnelling engineering. The optimization of drilling parameters is one of the most important technologies of intelligent drilling while determining the key values to improve the drilling efficiency is still a difficulty. In this paper, we established a composite percussive rock breaking model of a full-size polycrystalline diamond compact (PDC) bit based on the continuous–discontinuous element method (CDEM). The evolution of rock fragmentation under the action of composite impact is analysed by the rock breaking volume and the torque applied on the drill bit during drilling. Based on actual construction, two key parameters, axial impact velocity and impact frequency, affecting the process of composite impact rock breaking were selected, to further investigate their effect on composite impact rock breaking. The fitting curves obtained from the sensitivity analysis of the rock breaking effect under different construction parameters were proposed to guide the intelligent drilling. Furthermore, the construction parameters of a diversion inclined shaft in the #2 construction branch hole of Tiantai Mountain pumped storage power station in Zhejiang Province were collected, which provides validation data for the proposed optimizing theory of the drilling parameters.

Fire accident seriously threatens the stable operation and personnel security in the subway station, and thermal stratification is an important factor used to determine smoke settlement. Therefore, this paper carried out a series of model experiments by altering the heat release rate (HRR), fire locations, and ventilation conditions. The vertical temperature of the central longitudinal axis was measured, and the S value representing that thermal stratification characteristic was calculated and compared under various fire scenarios. The results showed that the temperature changing curve in the vertical direction was significantly affected by ventilation volume, but it had little to do with the HRR and fire location. The increase of HRR could influence the value of S by enhancing the upper temperature for the whole space and heating the lower temperature near the fire. In addition, the S value decreased with the distance under different longitudinal fire source locations, whereas the variety of S was not monotonical with the deviating transverse distance. And the larger ventilation volume was conducive to restricting smoke diffusion and improving the value of S. This work could provide data support and theoretical reference for controlling smoke stratification under subway station fire.

With the development of urban transportation, metros have become an important means of travel for residents. However, casualty and economic loss might occur in metro systems due to various emergencies. Social media has gradually become the main way to express people’s needs, which provides a new analysis perspective for risk management in metros. This study takes the Zhengzhou Metro flood as an example and collects relevant social media data. Then, the analysis method of social media data evolution characteristics in metro emergencies is proposed. Finally, the evolution characteristics of social media data are analyzed from three aspects: spatiotemporal distribution, emotional distribution, and hot topics classification. The results show that: The temporal distribution of social media data is affected by the emergency process and official media; the spatial distribution of social media data reflects the distribution of stations affected by emergency and temporary shelters; timely and appropriate official media reports are conducive to guiding public emotions toward positive; and the key hot topics can be divided into disaster environment (DE), disaster impact (DI), disaster carrier (DC), emergency management (EM), positive comment (PC), and negative comment (NC). The proposed method can provide support for public opinion analysis and risk management in metro emergencies.

Voids behind tunnel linings are critical factors affecting tunnels’ safety and durability. For automatic, rapid, and accurate detection of void defects behind tunnel linings, this paper proposes an intelligent recognition method of void detection based on deep learning (DL) and percussion method. Extensive indoor percussion experiments were first conducted to obtain a total of 77,925 percussion signals. Afterward, the mel-frequency cepstrum coefficients (MFCCs) are utilized for signal feature extraction, based on which a convolutional neural network (CNN) is developed for automatic void defect diagnosis. The void automated diagnosis tests are subsequently performed, and the impact of three key factors on the recognition results is investigated. The results show that the proposed CNN can accurately identify voids ranging from 0.10 to 0.30 m, with an average accuracy of 94.96% and an F1 score of 72.29%. The exploration of the slab thickness indicates that the proposed method is capable of detecting voids with an average accuracy of 94.37% and an F1 score of 74.55%, with the slab thicknesses ranging from 0.10 to 0.30 m. Furthermore, the boundary effects of concrete slabs are analyzed. Finally, an on-site validation is carried out, and the good agreements between the developed method and ultrasonic detection method indicate that the CNN-aided percussion method is feasible in practical tunnel lining void inspection tasks.

The Three Gorges Project on the Yangtze River is a key pillar project for harnessing, developing, and protecting the Yangtze River. It is the world’s largest water conservancy complex as well as a landmark project representing the great rejuvenation of the Chinese nation. This section presents the case study of the Three Gorges Project. First, the project highlights and characteristics were summarized. Second, the survey and design, engineering construction, and scientific and technological research and design were presented. Third, the operation benefits of the Three Gorges Water Conservancy Complex were discussed. Finally, engineering pictures are provided.

The acoustic emission (AE) technique is suitable for monitoring and evaluating hydraulic concrete damage due to its good response to material damage. While continuously advancing conventional AE analysis methods, various advanced digital processing technologies and intelligent algorithms have been applied to deeply explore the damage information and evaluate hydraulic concrete damage. An intelligent framework for evaluating hydraulic concrete damage based on AE has been established, according to the working principle of the AE monitoring system for hydraulic concrete damage. Based on the content involved in this framework, a review is conducted on the current research status of hot topics such as conventional analysis methods, signal processing methods, acoustic source localization (ASL) methods, AE source recognition methods, and deep learning technique applications. The complex characteristics of AE signals of hydraulic concrete damage and the research needs of how to overcome the adverse effects have been summarized, aiming to continuously improve the framework and achieve the construction of an intelligent platform for evaluating hydraulic concrete damage based on AE.

This study investigated the behavior of a non-corroded reinforced concrete (RC) beam and two RC beams with non-uniform corrosion (whole span and side span) in a four-point bending test. The corrosion distributions of the tensile steel rebars were measured by two methods: diameter loss and mass loss. The peak values of corrosion distribution in the diameter-loss measurements were much greater than those in the mass-loss measurements. However, the average corrosion according to the diameter measurements was smaller than that in the mass-loss measurements. The corrosion distribution trends were the same in the two methods, indicating good measurement accuracy. From the bending test results, it appears that increasing corrosion area and corrosion ratios reduce the beams’ stiffness, yielding capacity, and ultimate capacity. All three specimens had similar failure patterns: steel yielding followed by compression failure of the concrete. A nonlinear three-dimensional (3D) analysis model that considered the non-uniform corrosion of the rebars was established and verified by experimental data. The model’s predictions for stiffness, ultimate capacity, and failure patterns were consistent with experimental results. The spatial variability factor (R'), which is the ratio of the maximum corrosion ratio (γmax) to the average corrosion ratio (γave), was adopted to quantify the non-uniformity of the corrosion distribution in this study.

Highly accurate microseismic (MS) localization is the basis for rock damage assessment and disaster warning. The engineering background noise mixed in the MS signal (s(ε)) seriously affects the subsequent analysis of the MS signal. A noise reduction method of singular spectral analysis–complementary ensemble empirical mode decomposition–wavelet threshold (SSA–CEEMD–WT) is proposed. The CEEMD, CEEMD–WT, and proposed methods are used for denoising the noisy Ricker wavelet. The signal-to-noise ratio (SNR) of the denoised signal (xde(ε)) by the proposed method is 56.77% and 37.88% higher than those of CEEMD and CEEMD–WT methods, respectively. Moreover, an adaptive artificial bee colony (ABC) algorithm is applied for MS source (O(h0, y0, z0)) location. The time to quantile difference is introduced as the objective function. The blast positioning test results prove that the proposed method improves the positioning accuracy of particle swarm optimization (PSO) algorithm and simulated annealing PSO (SA-PSO) algorithm by 44.12% and 47.64%, respectively. The MS positions of underground caverns reveal that the calculated clusters of MS events using the adaptive ABC algorithm are more concentrated at the structural plane and appearance deformation failure location and in good agreement with field survey and routine monitoring data.

The air-cooled condenser (ACC) technology drives the decoupling of China’s water consumption and energy production. However, the optimal cleaning frequency of the ACC system has yet to be thoroughly studied. We develop a theoretical model for the total cost of the dust-fouling energy loss and direct cleaning service costs. This extended model is the first to consider energy loss in the cleaning and production phases with field validation. The cleaning period is optimized to minimize the total cost. Numerical solutions are sought to demonstrate the relationship between the normalized optimized cleaning period and the dimensionless inputs. An empirical fitting equation is developed for convenient use in industrial applications. An innovative variance-based global sensitivity analysis (SA) is performed to estimate the sensitivity of the optimization result to the input parameters. We found that heat resistance (Rf), installed capacity, utilization rate, grid electricity price (Enet), and cleaning cost rate have substantial impacts. The present study has the potential to improve the cleaning service plan of the onsite maintenance, to provide a theoretical framework for the life cycle analysis of the power plant, and to inform the decision-makers of the priority of data collection and sensor network deployment.

The rapid advancement of information technologies, such as cloud computing, internet of things (IoT), and big data, has significantly enhanced the intelligence level in dam construction. Intelligent monitoring systems for dam filling have garnered considerable attention from water conservancy and hydropower engineering units, owing to their capability for refined management and quality control of layered and compartmentalized construction processes. The construction of earth–rock dams has undergone a transformation, shifting from manual management of dam compaction equipment, layer management, and quality control to a comprehensive intelligent approach. This new approach encompasses fine-grained control of unit engineering, dam material identification, unmanned driving technology, real-time assessment and monitoring of compaction quality, and early warning forecasting. This article presents a systematic overview of key technologies employed in the intelligent construction process of earth–rock dams. These technologies include the fine decomposition of building information modeling (BIM) models on web platforms, rapid identification of dam material particle size, the application of autonomous driving technology for intelligent dam filling, and real-time monitoring and evaluation of dam compaction characteristics. Additionally, the paper discusses the latest research results in the field, building upon existing technology progress, and concludes by exploring the future development trends of intelligent dam construction. The construction of intelligent earth–rock dams serve as a crucial reference and valuable lessons for the development and construction of high earth–rock dam projects.