《1 Engineering research fronts》

1 Engineering research fronts

《1.1 Trends in Top 10 engineering research fronts》

1.1 Trends in Top 10 engineering research fronts

The Top 10 engineering research fronts in the field of civil, hydraulic, and architectural engineering are summarized in Table 1.1.1. These fronts cover a variety of disciplines, including structural engineering, architectural design and theory, geological engineering, transportation engineering, municipal engineering, hydraulic engineering, urban planning and landscaping, and surveying and mapping engineering. The experts nominated three of these fronts: “performance perception assessment and rehabilitation of in- service road, rail, and airport infrastructure”, “multi-scale spatial optimization of high-density urban built environment guided by safety and resilience”, and “risk identification and control of pathogenic microorganisms in urban water system”. The other seven were identified using the co-citation clustering method applied to the top 10% of highly cited papers, and they were agreed, in expert panel meetings, to be worthy of places in the Top 10. Table 1.1.2 presents annual statistics on the core papers published between 2017 and 2022 that are relevant to these Top 10 research fronts.

(1) AI-based structural damage identification and performance prediction

AI-based structural damage identification and performance prediction is a technology that aims to build intelligent models by integrating cutting-edge information technologies, methods and equipment, such as the Internet of Things, big data, machine learning, etc., for the accurate identification of structural damage and the prediction of performance under different loadings and environmental conditions. It is also possible for the model to conduct self-learning and reinforcement based on fusion of real-time

《Table 1.1.1》

Table 1.1.1 Top 10 engineering research fronts in civil, hydraulic, and architectural engineering

《Table 1.1.2》

Table 1.1.2 Annual number of core papers published for the Top 10 engineering research fronts in civil, hydraulic, and architectural engineering

data from structural responses and from radar/vision/sound sensors. The research on AI-based structural damage identification and performance prediction is of great significance, as it can improve the functionality, economy, reliability and safety of structures during their life cycles. The major topics covered by this research front include: ① new sensing equipment for structural damage detection and monitoring; ② standardization and fusion of structural health monitoring data; ③ structural damage detection and localization based on machine learning algorithms; ④ data-driven structural performance prediction methods; and ⑤ physics-informed intelligent models for structural performance prediction. The main development trend is to build a system for the holographic perception of structural status and providing intelligent diagnosis, and to improve ability in structural damage identification and performance prediction models in terms of interpretability, generalization, accuracy, etc. With the integration of physical information and prior knowledge, models can be evolved and widely applied in engineering projects in complex scenarios. Between 2017 and 2022, 54 core papers relevant to this research front were published. These papers received 3 616 citations, with an average of 66.96 citations per paper.

(2) Methods and technologies for carbon-emission reduction in urban regeneration

The methods and techniques for carbon-emission reduction in urban regeneration primarily refer to the integration of strategies to reduce carbon emissions relating to urban renewal design. These methods and techniques introduce carbon-emission reduction approaches and technologies into the process of updating various urban elements, thereby significantly promoting low-carbon development and sustainability in cities. The primary technological directions encompass: ① simulation of energy consumption and microclimate environments in existing urban areas; ② optimization of urban regeneration design schemes and decision- making tools that are subject to carbon reduction goals; ③ integration of carbon-emission reduction technologies with building materials and innovative construction techniques in urban regeneration; and ④ integrated design of carbon-emission reduction technologies for diverse types and scales of urban regeneration elements. The development trend in the field involves integrating interdisciplinary knowledge such as building energy-saving technologies, renewable energy technologies, geographic information science, computer science, and artificial intelligence. This integration facilitates the blending of urban regeneration elements with carbon-emission reduction technologies. Furthermore, the development of digital platforms allows for multi-scenario simulations, application, and monitoring feedback, effectively supporting sustainable and organic urban regeneration. Between 2017 and 2022, 45 core papers relevant to this research front were published. These papers received 2 229 citations, with an average of 49.53 citations per paper.

(3) Spatio-temporal distribution and intelligent evaluation of giant geological disaster chains

The giant geological disaster chain refers to a series of geological disaster events, which include one or more secondary disasters induced by an initial event, directly or indirectly. Compared to a single geological disaster, a giant geological disaster chain shows significant uncertainty. It features a complex formation mechanism with multiple disasters affecting each other and a disaster- enlarging effect caused by the chain-like cumulative amplification process. In recent years, the engineering constructions in high- altitude and cold regions in China, represented by the Sichuan-Tibet Railway, have been facing the threat of giant geological disaster chains. The disaster scale has reached hundreds of millions cubic meters and serious casualties and economic losses have resulted. Accurately predicting the spatio-temporal distribution of such chains and intelligently assessing their ongoing disaster risks is crucial for ensuring the safety, economy, and sustainability of engineering constructions. The current research focuses on: ① spatio-temporal distribution characteristics and intelligent identification of geological disaster chains in complex disaster prone environments; ② formation and evolution mechanism of geological disaster chains and establishment of dynamic models; ③ resilience and risk assessment and optimization methods for preventive structures of geological disaster chains; and ④ real time monitoring and warning, intelligent evaluation and response to preventive decisions for large-scale giant geological disaster chains. The development trend involves distinguishing the spatio-temporal distribution pattern of giant geological disaster chains and clarifying the catastrophe mechanism. On this basis, multi-source data and intelligent algorithms can be integrated to predict the development trend of geological disaster chains and to assess potential risks and impacts. Meanwhile, a real-time monitoring and early warning system needs to be developed based on intelligent technology. At last, an intelligent risk assessment, prevention, control and decision-making system should be built for large-scale and giant geological disaster chains. Between 2017 and 2022, 109 core papers relevant to this research front were published. These papers received 6 081 citations, with an average of 55.79 citations per paper.

(4) Performance perception assessment and rehabilitation of in-service road, rail, and airport infrastructure

The assessment and rehabilitation of in-service road, rail, and airport infrastructure involve the utilization of intelligent perception technologies and comprehensive evaluation methodologies for real-time monitoring, assessment, and prediction of the performance status of existing transportation infrastructure such as roads, railway tracks, and airport facilities. This is essential for accommodating the growing demand for passenger and freight transportation by facilitating necessary improvements and expansions to enhance safety, reliability, and sustainability of transportation infrastructure and adapt to the continually evolving environment and user needs. Compared to new infrastructure development, repair and expansion of in-service road, rail, and airport projects are confronted with more intricate temporal, spatial, and ecological constraints. Research directions within this field encompass the following: ① structural health monitoring and assessment, i.e., utilizing sensor technologies, non-destructive testing methods, and other tools to perform real-time monitoring and comprehensive assessment in order to detect potential structural issues, prevent accidents with efficient and precise assessment, and enhance structural and functional durability; ② traffic load perception and optimization, i.e., leveraging intelligent transportation systems, with big data analytics, and related methodologies to real-time perceived traffic flows for accurate traffic management and scheduling; ③ environmental assessment and enhancement, i.e., monitoring and analyzing factors such as climate and geology to study the impact of traffic loads and environmental changes on structures for optimizing the design and maintenance strategies of transportation infrastructure and ultimately enhancing durability and adaptability; and ④ rehabilitation and expansion planning and design, i.e., integrating analysis of structural in-service state evolution patterns and performance perception assessment to formulate rational plans for facility rehabilitation and expansion, aligning with future transportation development demands. Future developments in this field are anticipated to concentrate on the following areas: high-precision intelligent perception and assessment; sustainability and environmentally friendly low-carbon design; comprehensive performance optimization algorithms and decision models; digital construction and engineering management. Between 2017 and 2022, 25 core papers relevant to this research front were published.

These papers received 852 citations, with an average of 34.08 citations per paper.

(5) Life-cycle disaster resilience of structural and engineering systems

The life-cycle disaster resilience of structural and engineering systems refers to the evaluation and improvement of their resilience to natural and man-made disasters throughout their entire life cycle, including design, construction, maintenance, and demolition. Traditional research on disaster resilience of structural and engineering systems is mainly focused on design and construction stages, with less attention paid to maintenance and demolition stages. As a great number of structural and engineering systems accumulate in various countries around the world, research on disaster resilience of existing structural and engineering systems has significant social significance and strategic value for the construction of resilient cities. Its main research directions include: ① assessment of life-cycle disaster resistance and resilience improvement of a single structure; ② assessment and improvement of disaster resilience of building systems throughout their entire lifespan; ③ assessment and improvement of disaster resilience of lifeline systems such as water, electricity, gas and communication networks; and ④ construction of resilient cities with multiple systems for considering disaster resilience throughout infrastructure lifespan. The main development trend for future research relates to the life-cycle damage mechanism, resilience assessment and resilience improvement for complex and giant systems. Based on this, combined with health monitoring, big data and artificial intelligence, accurate assessment and significant improvement of the life-cycle disaster resilience of urban engineering systems can be achieved. At the same time, the development of intelligence of urban engineering system resilience and disaster prevention can be accelerated, and the disaster resistance safety of urban engineering systems can be improved. This will also lead to establishment of a resilient disaster prevention and control system for the entire lifespan of urban engineering systems. Between 2017 and 2022, 37 core papers relevant to this research front were published. These papers received 1 456 citations, with an average of 39.35 citations per paper.

(6) Co-fermentation of municipal sludge and refuse for efficient resource utilization

Municipal sludge is the inevitable product of municipal sewage treatment, with the dual attributes of “pollution” and “resource”. Harmless treatment and resource utilization of sludge are the key measures for promotion of the synergy between pollution control and carbon-emission reduction in the field of water pollution prevention and control. The collaborative anaerobic fermentation treatment of municipal sludge and organic wastes such as refuse can produce significant economic and environmental benefits, which is conducive to improving the degradation efficiency of organic matter and the stability of fermentation systems, and significantly increasing the output of high-value products. Thus, the co-fermentation of municipal sludge and refuse is an effective way to realize the harmless treatment and resource utilization of municipal sludge. The current research focuses on: ① the co- fermentation mechanism and resource efficiency of municipal sludge and refuse under different conditions; ② optimization and control technology of municipal sludge and refuse co-fermentation; ③ the high-value directional transformation mechanism of municipal sludge and refuse co-fermentation; and ④ research and development of municipal sludge and waste co-fermentation equipment. The main development trend in the future is gradual clarification of the influencing factors regarding urban sludge and waste co-fermentation efficiency and energy consumption. On this basis, attention is placed on optimization of the resource path of the municipal sludge and refuse co-fermentation process, strengthening the production of new clean biofuels in the co- fermentation process, further reducing the cost of co-fermentation, and building an efficient resource technology and equipment system of municipal sludge and refuse co-fermentation. Between 2017 and 2022, 73 core papers relevant to this research front were published. These papers received 3 012 citations, with an average of 41.26 citations per paper.

(7) Coordinated evolution of groundwater quantity, quality and environmental impact and groundwater sustainable utilization

Groundwater plays a critical role in the urban and rural water supply, economic and social development, and ecological and environmental maintenance in China. The coordinated progress relating to groundwater quantity, quality and environmental impact concerns the interactions and change in groundwater resources, involving water quality and ecological and geological environmental effects. The sustainable utilization of groundwater is aimed at ensuring that groundwater resources can meet the long-term stable development of human society and the whole environmental system, and can avoid ecological and geological problems and disasters caused by over-exploitation. China’s groundwater resources are under great threat due to increased human activity and climate change. The current research focuses on: ① theoretical research of groundwater circulation and distribution, and pollutant migration and transformation; ② development of methodology of groundwater monitoring-simulation-evaluation; ③ technological investigation of groundwater over-exploitation prevention and groundwater remediation; and ④ management of sustainable utilization of groundwater. Future development trends include the following: ① addressing the challenging theoretical task of groundwater circulation as well as the migration and transformation of material and energy in a changing environment; ② developing new methods for air-space-ground integrated groundwater monitoring and interpretation; ③ establishing multi- scale multi-process technical systems for groundwater simulation and prediction and groundwater quantity-quality-environmental impact evaluation; ④ to exploring key technologies for warning, control and prevention of groundwater over-exploitation and its secondary disasters; ⑤ completing key technical challenges on contamination source identification and ecological restoration; ⑥ creating multi-source groundwater storage and sustainable utilization technologies in the continent and ocean; and ⑦ proposing multi-objective optimization management system for different scenarios and groundwater resource regulation measurements and strategies. Between 2017 and 2022, 72 core papers relevant to this research front were published. These papers received 5 307 citations, with an average of 73.71 citations per paper.

(8) Multi-scale spatial optimization of high-density urban built environment guided by safety and resilience

The high-density urban built environment, guided by requirements for safety and resilience, concerns urban planning and design that prioritizes the creation of a living environment that is both secure, disaster-resistant, and sustainable under conditions of high population density. Unlike conventional cities, high-density cities face distinct challenges such as heightened safety risks associated with high-risk buildings, suboptimal performance of disaster-resistant building, low standards of evacuation of buildings, and inadequate building maintenance systems. In recent years, China’s increased frequency of extreme weather and climate events, has subjected the country’s high-density urban disaster prevention systems to rigorous testing. Consequently, the safety and resilience of high-density urban development have formed the basis of a critical research trend. It is of great importance to explore typical disaster scenarios related to high-risk, disaster-resistant, and disaster-avoidance buildings and bolstering urban resilience to effectively manage the profound impacts of climate change. The current research focuses on: ① reevaluation and reformulation of building codes tailored to high-density cities; ② ongoing dynamic monitoring and assessment of buildings within high-density urban areas; ③ enhancement of the resilience and sustainability of buildings in high-density environments; ④ the renewal and renovation of high-density urban building stocks; and ⑤ optimizing disaster resilience in high-density urban and rural built environments within land-sea integrated regions. The development trend of research is to identify vulnerabilities and to gauge the quality of the built environment in high-density cities. On this basis, a combination of research into safety hazards and resilience, coupled with multi-scale analysis and predictive assessment of the myriad risks inherent in mega-city environments, will drive the optimization of high-density urban settings from the perspective of sustainable development. Advanced methodologies like deep learning, data mining, and digital twin technology will be harnessed for this purpose. Between 2017 and 2022, 16 core papers relevant to this research front were published. These papers received 681 citations, with an average of 42.56 citations per paper.

(9) Risk identification and control of pathogenic microorganisms in urban water systems

Urban water environment is closely related to natural circulation and human production activities. There are risks of outbreak and spread of pathogenic microorganisms such as pathogens and viruses in urban water system, mainly from human and animal feces, garbage, domestic sewage and hospital sewage. The migration and spread of pathogenic microorganisms in urban water systems can lead to outbreak of epidemic diseases, posing a serious threat to environmental safety and public health. Effective identification, control, and deep reduction of pathogenic microorganisms in urban water systems, as well as ensuring water safety, are urgent issues that need to be addressed. The main technical directions include: ① building a basic database of the specific types, distribution, transmission patterns, and removal pathways of pathogenic microorganisms in urban rivers/lakes; ② rapid identification, characterization, and risk assessment of pathogenic microorganisms in the water quality monitoring and evaluation system; ③ the inactivation of pathogenic microorganisms by conventional disinfection techniques (chlorine based disinfectants, ozone, UV, etc.) and combined disinfectants; ④ development of novel membrane separation technologies and adsorbents based on the principles of physical separation and adsorption; ⑤ microbial metabolism technologies such as activated sludge process, membrane bio-reactor (MBR) process, etc.; and ⑥ progress towards a comprehensive risk management and control technology system for pathogenic microorganisms, including water source control, water plant removal, and pipeline network protection. Facing the ecological security of urban water systems, future research focuses and challenges mainly include building a database of resistance groups/pathogens/pathogenic viruses in typical water systems, developing a list of water borne pathogenic microorganisms for priority control in river basins, establishing indicative pathogenic microorganisms for health risk assessment, developing high-throughput detection methods with supporting equipment, and establishing a comprehensive control technology system. Between 2017 and 2022, 17 core papers relevant to this research front were published. These papers received 816 citations, with an average of 48.00 citations per paper.

(10) Intelligent object detection in high-resolution remote sensing images

Intelligent object detection methods in high-resolution remote sensing images utilize knowledge engineering, deep learning, logical reasoning, swarm intelligence, and other new artificial intelligence technologies and means, to obtain category and location information from high-resolution remote sensing images. Such technologies are widely used in military and civilian fields such as reconnaissance, surveillance, early warning, and search and rescue. Development trends in this research front include: ① general intelligent target detection methods, which aim to address the challenges faced in remote sensing image target detection, such as class imbalance, high background complexity, multi-scale changes in targets, special imaging perspectives, and small/micro object detection; and  ② development of specialized object intelligent detection methods in remote sensing images for significant targets such as airports, buildings, aircraft, ships, vehicles, clouds, and sea ice. The main development trend in the future will be a focus on the challenges of small object detection and multimodal object detection, continuously optimizing the intelligent object detection model, constructing an intelligent high-resolution remote sensing object detection method system guided by knowledge and based on algorithms, thereby promoting intelligent understanding of remote sensing scenes and providing support for the construction of remote sensing image intelligent interpretation systems. Between 2017 and 2022, 151 core papers relevant to this research front were published. These papers received 14 846 citations, with an average of 98.32 citations per paper.

《1.2 Interpretations for three key engineering research fronts》

1.2 Interpretations for three key engineering research fronts

1.2.1 AI-based structural damage identification and performance prediction

The damage of civil engineering structures is related to their health status, and directly affects their safety and serviceability. Traditional methods of structural damage identification and performance prediction usually rely on physical sensors and mechanical models, while using AI methods can extract complex damage feature patterns from large amounts of multi- source multi-modal data, and analyze the key parameters that characterize structural performances. Additionally, the model can perform self-learning and reinforcement based on real-time data, to improve the accuracy and robustness of the results in damage identification and performance prediction. The research on AI-based structural damage identification and performance prediction can provide a scientific basis for structural health monitoring, maintenance reinforcement and performance enhancement. This is of great significance in reducing infrastructure operation and maintenance costs, improving management and maintenance strategies, and optimizing the life-cycle design of structures. The major topics in this research field include:

1) New sensing equipment for structural damage detection and monitoring. Efforts are devoted to: ① achieving qualitative perception and quantitative determination of structural damage in different scenarios and scales using various sensing technologies,
such as visual and acoustic sensors, microwave radar, distributed Fiber Bragg Grating, nanomaterial sensors, etc.; ② developing new integrated equipment and the corresponding distributed intelligence technology for structural damage detection and monitoring; and ③ establishing intelligent structural damage detection and monitoring systems that are capable of serving in an autonomous, comprehensive, and efficient way.

2) Standardization and fusion of structural health monitoring data. For the development of AI algorithms and model optimization, studies are carried out to establish the standard for the acquisition, transmission, storage, and usage of structural monitoring data. Technologies such as knowledge graphs, Bayesian networks, probabilistic graphical models, etc., are applied for the aggregation of multi-source and multi-scale data, providing high-availability data for structural damage identification and performance prediction.

3) Structural damage detection and localization based on machine learning algorithms. Based on machine learning models such as deep learning, support vector machine, clustering algorithm, etc., the damage mechanism is studied and surrogate models are built for structural damage detection and localization. Meanwhile, the models are iterated autonomously for self-learning and reinforcement using real-time data for environmental and structural concerns, thereby improving the accuracy and reliability of damage identification.

4) Data-driven prediction methods of structural performance. Considering the complex influencing factors of performances and the scarce data feature of engineering structures, data processing methods are proposed for feature extraction and feature analysis, and the data dimension is reduced effectively and efficiently. By combining active learning, reinforcement learning, and transfer learning, data-driven models relating to key characteristic parameters are established to predict various aspects of structural performances, including the mechanical performance, disaster prevention and mitigation resilience, and life cycle performance.

5) Physics-informed intelligent models for structural performance prediction. By the development of data, knowledge, models, and intelligent algorithms, mechanical theories and experts’ priori knowledge can be introduced to propose a hybrid paradigm of physics-informed intelligent modeling for structural performance prediction. It is important for AI models to break through the bottleneck of the data dependency of model training and the deficiency in interpretability and generalization ability due to the “black box”. Such a breakthrough can drastically improve the accuracy and practicability of the data-driven model for performance prediction on engineering structures.

As shown in Table 1.1.1, 54 core papers concerning “AI-based structural damage identification and performance prediction” were published between 2017 and 2022, with each paper being cited 66.96 times on average. The top five countries in terms of output of core papers on this topic are Vietnam, Belgium, China, Republic of Korea, and the USA (Table 1.2.1). China is one of the most active countries, having published 25.93% of the core papers. The five countries with the highest average citations were the USA, Algeria, Belgium, Vietnam, and Japan. The papers published by Chinese authors were cited 66.36 times on average, which is slightly below the overall average. As illustrated by the international collaborative network depicted in Figure 1.2.1, close cooperation was observed among the ten most productive countries.

The five institutions that published the most core papers were Ghent University, Ton Duc Thang University, Ho Chi Minh City Open University, Southeast University, and University of Transport & Communications (Table 1.2.2). In terms of number of publications, the top three institutions collaborated frequently, focusing on AI models for structural damage identification. They proposed various algorithms to solve the inverse problems in structural health monitoring, i.e. identifying structural damage based on responses such as structural stiffness, frequencies, and strain energy, etc. Their research primarily applies to laminated composite structures, and is also applicable in actual bridges. As illustrated in Figure 1.2.2, the ten most productive institutions have conducted collaborative studies in this regard.

As shown in Table 1.2.3, the five most active countries in terms of paper citing were China, the USA, Vietnam, Republic of Korea, and Iran. The top five institutions in terms of citing core papers were Tongji University, Southeast University, Ghent University, Ho Chi Minh City Open University, and Harbin Institute of Technology (Table 1.2.4). According to the citations of core papers, there were differences between the top five countries in terms of output of core papers and the five most active countries in terms of paper citations, which indicates that this front has received attention from scholars from different countries.

Based on the above statistics, the proportion of paper citations in China is far higher than the proportion of core papers from China, indicating that Chinese researchers pay close attention to this front.

In the next ten years, the key development direction in this research front is to build a holographic perception system for structure status, to establish a structure intelligent diagnosis system driven by multi-modal large models and to enhance model performance for structural damage identification and performance prediction. On one hand, these developments rely on highly-integrated intelligent perception technology, combined with multi-source multi-modal data fusion modeling, which significantly improves the capacity and quality of available data. On the other hand, the damage mechanism and performance evolution process of structures under multi-factor multi-objective conditions can be taken into account for the exploration of the new paradigm that combines knowledge-driven learning models and physics information. Hence, it can achieve the improvement of structural damage identification and performance prediction in terms of interpretability, generalization, accuracy, etc. Relevant research studies can be applied to the operation and maintenance of existing structures, as well as to the design and construction of new structures, thus there is a great potential of development and a wide range of practical scenarios (Figure 1.2.3).

《Table 1.2.1》

Table 1.2.1 Countries with the greatest output of core papers on “AI-based structural damage identification and performance prediction”

《Figure 1.2.1》

Figure 1.2.1 Collaboration network among major countries in the engineering research front of “AI-based structural damage identification and performance prediction”

《Table 1.2.2》

Table 1.2.2 Institutions with the greatest output of core papers on “AI-based structural damage identification and performance prediction”

《Figure 1.2.2》

Figure 1.2.2 Collaboration network among major institutions in the engineering research front of “AI-based structural damage identification and performance prediction”

《Table 1.2.3》

Table 1.2.3 Countries with the greatest output of citing papers on “AI-based structural damage identification and performance prediction”

《Table 1.2.4》

Table 1.2.4 Institutions with the greatest output of citing papers on “AI-based structural damage identification and performance prediction”

《Figure 1.2.3》

Figure 1.2.3 Roadmap of the engineering research front of “AI-based structural damage identification and performance prediction”

1.2.2 Methods and technologies for carbon-emission reduction in urban regeneration

Urban regeneration, optimizing the physical conditions, spatial forms, and functions of the entire urban system, plays a crucial role in improving the currently high-energy consumption and high-carbon-emitting components of cities. It offers pivotal intervention for reducing urban carbon dioxide emissions. However, the current approach to urban regeneration primarily relies on traditional models, which face challenges such as difficulty in integrating urban regeneration targets with carbon-emission reduction technologies, limited perspectives on integration, low digital precision, and a lack of practical applications.

Therefore, the forefront and focus of research and development in urban regeneration methods and technologies lie in establishing systematic integration and collaborative mechanisms for applying various carbon-emission reduction technologies in urban regeneration. This involves exploring methods utilizing machine learning, genetic algorithms, and other techniques to optimize design and decision-making processes, as well as innovation of the integration of high- performance building materials with carbon-emission reduction technologies. The primary technological directions encompass:

1) Simulation of energy consumption and microclimate environment in existing urban areas. This involves leveraging extracted building big data information and relevant standard specifications, combined with GIS and building energy simulation tools, to automatically generate regional models of building energy consumption and carbon emissions. These models enable rapid modeling at the regional scale and can be automatically calibrated based on real-world data.

2) Optimization of urban regeneration design schemes and decision-making tools under carbon reduction goals. This involves utilizing advanced technologies and methods such as machine learning, big data analytics, and genetic algorithms to develop systems and platforms for scenario simulation and low-carbon assessment. These systems and platforms identify the current carbon dioxide emissions status and reduction potential of different urban regeneration measures, and generate the most suitable urban regeneration schemes through multi-objective optimization.

3) Integration of carbon-emission reduction technologies with building materials and innovative construction techniques in urban regeneration. The construction industry is considered a major consumer of raw materials and energy. In the maintenance and renovation of existing buildings and infrastructure, the application of high-performance building materials and innovative construction techniques integrated with carbon-emission reduction technologies can prove advantageous in addressing the challenges of low energy efficiency and high carbon emissions faced by the construction industry.

4) Integrated design of carbon reduction technologies for diverse types and scales of urban regeneration elements. Urban regeneration involves comprehensive and complex system engineering projects. In addition to in-depth research on the application of carbon-emission reduction technologies to individual urban regeneration elements, it is essential to establish the coupling of emission reduction technologies to utilization by various urban elements. This includes areas such as energy, buildings, transportation, waste management, and carbon sequestration techniques.

As shown in Table 1.1.1, 45 core papers concerning “methods and technologies for carbon-emission reduction in urban regeneration” were published between 2017 and 2022, with each paper being cited 49.53 times on average. The top five countries in terms of output of core papers on this topic are China, the USA, the UK, Italy, and Australia (Table 1.2.5). China is the most active country, having published 55.56% of the core papers. The five countries with the highest citations per paper were China, Australia, the USA, Singapore, and Israel. The papers published by Chinese authors were cited 62.40 times on average, which is above the overall average. As illustrated by the international collaborative network depicted in Figure 1.2.4, close cooperation was observed among the ten most productive countries.

The five institutions that published the most core papers were China University of Mining & Technology, East China Normal University, Wuhan University, Shanghai Jiao Tong University, and University of Shanghai for Science and Technology (Table 1.2.6). The forefront directions at China University of Mining and Technology primarily include research on the development of renewable energy, smart city policies, and industrial integration to enhance energy efficiency, reduce carbon emissions, and mitigation of the impact of air pollution. This research aims to provide empirical evidence and policy insights. At East China Normal University, the forefront directions mainly involve studying the spatio-temporal variations of CO₂ from a multi-scale perspective and examining the impact of government policies on ecological efficiency. This research aims to provide scientific foundations for feasible CO₂ emission reduction policies. At Wuhan University, the forefront directions focus on studying the distribution characteristics and deployment potential of different renewable energy sources, as well as the influencing mechanisms on their power generation efficiency. Additionally, attention is given to the spatio-temporal distribution patterns of PM2.5, CO₂, and their influencing factors. As illustrated in Figure 1.2.5, the ten most productive institutions have conducted collaborative studies in this regard.

As shown in Table 1.2.7, the five most active countries in terms of citations were China, the USA, the UK, Australia, and Italy. The top five institutions in terms of citations were Chinese Academy of Sciences, China University of Mining & Technology, Chongqing University, Wuhan University, and Tsinghua University (Table 1.2.8). China ranked first in the quantity of core papers produced and the number of citations of core papers, indicating that Chinese researchers pay close attention to this front.

Summarizing the above statistics, Chinese scholars have performed well and become leaders in the research of “methods and technologies for carbon-emission reduction in urban regeneration”.

The key development directions for the forefront of “Methods and technologies for carbon-emission reduction in urban regeneration” in the next 5−10 years will be as follows: the development and application of carbon reduction materials in urban regeneration; integration of new energy and building technologies; optimization of low-carbon design in urban regeneration; integration of carbon-emission reduction technologies; application of digital technologies and verification of carbon-emission reduction effectiveness.

Specifically, in the construction and computation of energy consumption and carbon emissions simulations, efforts will be made to improve the accuracy of precise accounting and dynamic prediction of carbon emissions from urban-scale building clusters, catering to different scales of urban regeneration design.

In terms of technology integration and application, there will be a focus on promoting iterative updates of individual carbon- emission reduction engineering technologies in urban and neighborhood renewal projects, while establishing integrated carbon- emission reduction technologies for multi-system coupling in urban regeneration projects.

Regarding the development of decision support tools for low-carbon urban regeneration, the emphasis will be on establishing comprehensive platforms for optimizing solutions with various emission reduction technologies, and clarifying the “low-carbon” goals, indicators, and technological pathways for different regions and stages of urban development (Figure 1.2.6).

《Table 1.2.5》

Table 1.2.5 Countries with the greatest output of core papers on “methods and technologies for carbon-emission reduction in urban

《Figure 1.2.4》

Figure 1.2.4 Collaboration network among major countries in the engineering research front of “methods and technologies for carbon-emission reduction in urban regeneration”

《Table 1.2.6》

Table 1.2.6 Institutions with the greatest output of core papers on “methods and technologies for carbon-emission reduction in urban

《Figure 1.2.5》

Figure 1.2.5 Collaboration network among major institutions in the engineering research front of “methods and technologies for carbon-emission reduction in urban regeneration”

《Table 1.2.7》

Table 1.2.7 Countries with the greatest output of citing papers on “methods and technologies for carbon-emission reduction in urban regeneration”

《Table 1.2.8》

Table 1.2.8 Institutions with the greatest output of citing papers on “methods and technologies for carbon-emission reduction in urban regeneration”

1.2.3 Spatio-temporal distribution and intelligent evaluation of giant geological disaster chains

Giant geological disaster chains often occur in high-altitude frigid mountainous areas worldwide. With the increase of construction scale, major engineering constructions around the world gradually expand to those regions with harsh geological conditions. However, complex terrain and geomorphic conditions, dense regional fault zones, and frequent strong earthquake activities pose significant disaster risks to engineering construction and safe operation. In addition, as “amplifiers” of global warming, high-altitude frigid mountainous areas have been affected by frequent extreme climate events worldwide in recent years, accelerating the occurrence of giant geological disaster chains, posing huge challenges to major engineering construction, operation, and management. Studying the spatio-temporal distribution and intelligent evaluation of giant geological disaster chains is of great significance for ensuring safe production and accelerating infrastructure construction.

《Figure 1.2.6》

Figure 1.2.6 Roadmap of the engineering research front of “methods and technologies for carbon-emission reduction in urban regeneration”

Currently, the major topics of this research front include:

1) The spatio-temporal distribution characteristics and intelligent identification of geological disaster chains in complex disaster- prone environments. The geological disaster chain often occurs in high-altitude remote mountainous areas. The following issues
are of interest: ① combining multi-source heterogeneous data interpretation algorithms based on the comprehensive remote sensing technology of multiple sources such as satellite, aerial, ground, and underground; ② establishing a database of geological disaster chains in mountainous areas to reveal the relationship between the formation of geological disasters and complex disaster gestation factors; ③ identifying the main disaster causing factors and summarizing the spatio-temporal development and distribution patterns of geological disaster chains; ④ establishing early identification markers for different types of geological disaster chains by machine learning image recognition technology; and ⑤ realizing intelligent identification of key sections of potential mutual transformation in the geological disaster chain.

2) The construction of a dynamic model for the genetic and chain evolution mechanism of geological disasters. Efforts are devoted to: ① the effects of complex disaster gestation conditions such as seismic motion, rainfall, and temperature on the mechanical properties of disaster bodies such as pore water pressure, stress, and strength; ② coupled studies from the perspectives of macroscopic geological dynamic processes and microscopic particle mechanical characteristics to reveal the critical mechanical conditions for the transformation of multiphase (solid-liquid) and multistage (flow-blockage-collapse) disasters; and ③ establishing a multi-scale coupled dynamic model that considers the macro and micro interconnected effects.

3) Establishment of resilience and risk assessment and preventive structure optimization methods for geological disaster chains. The aims are to: ① construct a random evaluation method for the disaster range to address the suddenness, uncertainty, and complex attributes of the giant geological disaster chain; ② conduct large-scale spatio-temporal risk prediction and risk zoning evaluation combined with artificial intelligence algorithms; ③ use a Pareto optimality method to achieve local optimization and adjustment of high-risk section lines; ④ evaluate the impact resistance performance of different types of protective structures with stochastic dynamic equations and obtain the stochastic dynamic response laws of engineering structural performance indicators; and ⑤ characterize the failure probability and vulnerability of engineering structures from the probability density function level and carry out structural design based on the concept of recoverability.

4) Real time monitoring and warning, intelligent evaluation, and response to preventive decisions for large-scale giant geological disaster chains. The aftermath of geological disaster chains often involves large-scale hazards across administrative regions (national boundaries, provincial boundaries, county boundaries), resulting in delay in disaster assessment and difficulty in
intelligent coordination of risk management. The requirement, therefore, is to: ① upgrade the risk assessment parameters of intelligent algorithms based on multi-source monitoring data and dynamic numerical simulation results; ② build an intelligent dynamic evaluation system and a standardized early warning system for the entire processes of geological disaster chains, including early identification, investigation and evaluation, monitoring and warning, and risk zoning; and ③ make different resilient risk prevention and control measures for different warning levels.

As shown in Table 1.1.1, 109 core papers concerning “spatio-temporal distribution and intelligent evaluation of giant geological disaster chains” were published between 2017 and 2022, with each paper being cited 55.79 times on average. The top five countries in terms of output of core papers on this topic are China, Australia, the USA, Italy, and the UK (Table 1.2.9). China is one of the most active countries, having published 77.06% of the core papers. The five countries with the highest citations per paper were Norway, India, Vietnam, Iran, and Australia. The papers published by Chinese authors were cited 56.32 times on average, which is above the overall average. As illustrated by the international collaborative network depicted in Figure 1.2.7, close cooperation was observed among the ten most productive countries.

The five institutions that published the most core papers were China University of Geosciences, Chengdu University of Technology, Nanchang University, Chinese Academy of Sciences, and Newcastle University (Table 1.2.10). China University of Geosciences has focused on landslide displacement prediction based on multi-source data fusion and artificial intelligence algorithms, Chengdu University of Technology and Nanchang University have focused on the landslide susceptibility prediction based on machine learning algorithms. As illustrated in Figure 1.2.8, the ten most productive institutions have conducted collaborative studies in this regard.

As shown in Table 1.2.11, the five most active countries in terms of paper citations were China, the USA, Italy, Iran, and India. The top five institutions in terms of citations of core papers were China University of Geosciences, Chinese Academy of Sciences, Chengdu University of Technology, Chang’an University, and Duy Tan University (Table 1.2.12). China ranked first in the quantity of core papers produced and the number of citations of core papers, indicating that Chinese researchers pay close attention to this front.

Based on the statistical data above, research on “spatio-temporal distribution and intelligent evaluation of giant geological disaster chains” is still in its infancy. Compared with foreign peers, Chinese scholars are in a leading position.

In the next decade, the frontier in this field must lie in the following areas: spatio-temporal development characteristics and early identification of geological disaster chains based on multi-source heterogeneous data analysis; the clarification of the genetic mechanism and chain evolution mechanism under multiple disaster factors; the establishment of resilient and risk assessment and preventive optimization methods for geological hazard chains under extreme climate change; and the promotion of real-time monitoring, warning, and intelligent disaster prevention decision-making system development. At the same time, this front will gradually develop towards quantification, intelligence, and systematization. With increasingly harsh geological environments and frequent extreme weather conditions encountered during engineering construction and operation, this cutting-edge research achievement will be widely applied in engineering construction and safe operation, with huge development potential (Figure 1.2.9).

《Table 1.2.9》

Table 1.2.9 Countries with the greatest output of core papers on “spatio-temporal distribution and intelligent evaluation of giant geological disaster chains”

《Figure 1.2.7》

Figure 1.2.7 Collaboration network among major countries in the engineering research front of “spatio-temporal distribution and intelligent evaluation of giant geological disaster chains”

《Table 1.2.10》

Table 1.2.10 Institutions with the greatest output of core papers on “spatio-temporal distribution and intelligent evaluation of giant geological disaster chains”

《Figure 1.2.8》

Figure 1.2.8 Collaboration network among major institutions in the engineering research front of “spatio-temporal distribution and intelligent evaluation of giant geological disaster chains”

《Table 1.2.11》

Table 1.2.11 Countries with the greatest output of citing papers on “spatio-temporal distribution and intelligent evaluation of giant geological disaster chains”

《Table 1.2.12》

Table 1.2.12 Institutions with the greatest output of citing papers on “spatio-temporal distribution and intelligent evaluation of giant geological disaster chains”

《Figure 1.2.9》

Figure 1.2.9 Roadmap of the engineering research front of “spatio-temporal distribution and intelligent evaluation of giant geological disaster chains”

《2 Engineering development fronts》

2 Engineering development fronts

《2.1 Trends in Top 10 engineering development fronts》

2.1 Trends in Top 10 engineering development fronts

The Top 10 engineering development fronts in the fields of civil, hydraulic, and architectural engineering are summarized in Table 2.1.1. These fronts cover a variety of disciplines, including municipal engineering, surveying and mapping engineering, urban planning and landscaping, architectural design and theory, transportation engineering, hydraulic engineering, construction materials, geotechnical and underground engineering, and structural engineering. The panel experts nominated the following development fronts: “intelligent detection and rehabilitation technology of drainage pipe leakage”, “core technology for 1 mm level global and regional coordinate frame”, and “advanced technologies for the construction and maintenance of road, rail, and airport infrastructure in extreme environments”. The remaining development fronts were identified from patent maps and agreed in the expert panel meetings. Table 2.1.2 presents annual statistics on patents registered between 2017 and 2022 related to these Top 10 development fronts.

(1) Intelligent detection and rehabilitation technology of drainage pipe leakage

Drainage pipe leakage has become one of the main bottlenecks restricting the safe discharge of stormwater and complete collection and treatment of wastewater. Intelligent detection and rehabilitation technology is designed to detect, diagnose and evaluate the level of structural defects in drainage pipe, and carry out low disturbance, high quality and proactive repair, which makes the drainage pipe run safely and efficiently. Moreover, along with the development of the Internet of Things, big data and cloud computing, the leakage detection technology will shift from periodic passive detection to proactive monitoring, providing support for preventive response to potential problems in drainage pipes. The main research areas include: ① digital base technology, including a domestic geographical information system and accurate measurement under complex medium conditions; ② intelligent detection technology, including identification of hidden risks and structural health monitoring via electromagnetic and sound waves; ③ smart assessment technology, including health prediction and adaptive modeling driving by data and models; and ④ efficient early-response technology suitable for large and extra-large drainage pipes, including availability of materials suitable for long distance transportation, corrosion environment and demanding mechanical conditions. The main development trend in the future mainly focuses on precise quantitative detection technology, high strength and high adaptability early-response technology, and detection and rehabilitation quality process control technology based on mobile Internet

《Table 2.1.1》

Table 2.1.1 Top 10 engineering development fronts in civil, hydraulic, and architectural engineering

《Table 2.1.2》

Table 2.1.2 Annual number of core patents published for the Top 10 engineering development fronts in civil, hydraulic, and architectural engineering