Search Results (121)

With the rapid growth of tourism in Dayi County over the past decade, this study develops a meteorological disaster risk assessment framework for major tourist attractions in this region. Drawing upon daily precipitation and temperature records from 25 meteorological stations (2014–2023) alongside multi-source geospatial data, we evaluate six primary attractions: Xiling Snow Mountain, Huashuiwan, Anren Ancient Town, Xinchang Ancient Town, Tianfu Huaxigu Valley, and Shujiu Cultural Park. The evaluation model integrates four core dimensions: hazard, environmental sensitivity, asset vulnerability, and disaster mitigation capacity. Indicator weights are determined through the Analytic Hierarchy Process, and GIS-based spatial analysis is employed for risk zonation. Additionally, the 45-year ChinaMet dataset provides independent validation for the long-term stability of the hazard assessment. Results reveal a distinct west-low, east-high composite risk gradient. High-altitude mountainous regions in the west exhibit a lower overall risk. Despite frequent extreme weather events, extensive vegetation coverage and low visitor density effectively buffer the negative impacts of physical hazards. Conversely, tourist attractions on the eastern plains fall within high-risk zones. Concentrated visitor populations, dense built environments, and low-lying terrain collectively amplify exposure to severe rainstorms and extreme heatwaves. These findings demonstrate that meteorological disaster risk in tourism destinations fundamentally arises from the deep coupling of natural and human systems. Thus, this study provides a scientific basis for implementing differentiated disaster prevention, mitigation, and localized emergency management strategies. Full article

Global urbanization is shifting from incremental expansion to stock optimization, and old residential communities have become important spatial units for low-carbon transition. However, in existing built environments, traditional process-based inventory methods face practical constraints, including missing original drawings, complex site conditions, and severe vegetation obstruction. As a result, systematic accounting of buildings, landscapes, and natural carbon sinks remains difficult. This study integrates life cycle assessment (LCA), BIM reverse modeling, 3D point clouds, DesignBuilder simulation, inventory-based accounting, and i-Tree Eco to construct a life cycle carbon emission accounting framework for old residential communities. The framework links current-condition data reconstruction, quantity take-off, operational energy simulation, landscape inventory accounting, and vegetation carbon sequestration assessment. It is applied to Nanyuan Xincun in Hefei to quantify the community-scale carbon source–sink structure. The results show that Nanyuan Xincun presents a clear operation-led emission pattern, with the operation and maintenance phase accounting for 82.52% of total positive emissions. Within architectural engineering, operation and maintenance accounts for 82.91%, while material production accounts for 13.28%. Landscape engineering shows a more mixed structure, with operation and maintenance accounting for 52.95% and material production accounting for 36.49%. Vegetation carbon sequestration analysis shows that mature trees and shrubs are the main ecological carbon assets. Annual sequestration reaches 16.95 t-CO₂e/a, and trees and shrubs contribute 92.85% of total vegetation carbon storage. Under current vegetation conditions, annual sequestration is equivalent to 32.99% of annual landscape operation emissions, indicating considerable ecological compensation potential. Based on these findings, this study proposes four optimization pathways: operational energy reduction, low-carbon material substitution, construction and demolition waste recycling, and mature tree protection. These pathways provide data support for refined carbon management and low-carbon renewal in existing communities. Full article

Urban drainage systems (UDSs) are critical built assets increasingly challenged by short-duration extreme rainfall, aging infrastructure, and rising surcharge risk. Physics-based hydrodynamic models are widely used for system assessment, but their high computational cost limits real-time operational prediction. Existing data-driven prediction approaches improve computational efficiency, but often rely mainly on sensor inputs and provide limited asset-level interpretation. This study develops an explainable digital twin for real-time prediction of storm-driven water level response in a separate sewer network in the Yangtze River Delta, China. The framework integrates 5 min monitoring and SCADA data, including water level, flow, pump status, and rainfall, with GIS and as-built asset information, including pipe geometry, hydraulic capacity, catchment characteristics, and network connectivity. A hybrid TCN-LSTM model was developed to predict water level and surcharge risk probability at 15–60 min lead times. A surrogate-based SHAP module was used to explain model predictions at the node and subcatchment scales. Multi-source fusion reduced the RMSE by approximately 18% compared with sensor-only baselines. The SHAP results showed that the pipe capacity-related variables and upstream contributing area were the main drivers of surcharge onset. The framework provides interpretable, operationally relevant predictions to support the resilience-oriented management of urban drainage systems. Full article

Road safety barriers represent a core component of the road with relevant consequences on effective safety for users. Maintaining these components in adequate conditions, within the quality admissibility thresholds, in compliance with all economic and management constraints, is a primary need for road administrators. In this paper, the authors propose an original procedure to classify the state of efficiency of road safety barriers, at the network scale and relying on conventional administrative data, in an optimized BIM environment, to simplify evaluations and management procedures. Through purpose-built algorithms based on selected geometric and functional parameters of the different road barriers, the algorithm provides a preliminary classification of the various segments, evidencing attention class indicators, useful as preliminary alert signals and for anticipating detailed investigations that can ensure significant economic efficiencies. The method was tested on a 10 km long motorway segment in Italy, evidencing the potential advantages of such an innovative approach to support, as a final goal, a comprehensive infrastructure digital model for virtual inspections, evaluating road component “health” state and properly implementing maintenance strategies. This approach improves network-scale monitoring and maintenance-related activity prioritization phases for road safety barriers, leveraging administrative data. This methodology functions as a BIM-based asset screening tool, as it offers a digital decision support system that identifies critical segments, to optimize the allocation of physical resources and prioritize on-site inspections where they are most needed. Full article

The form of a city evolves as the complexity of its systems increases. This study discusses how urban growth challenges have contributed to the deterioration of built environments and cultural heritage assets. It investigates how smart sustainable city (SSC) strategies have become significant policy instruments in regenerating Baghdad’s future built heritage and advancing the conservation of the city’s architectural heritage, infrastructure systems, and quality of life. The study aims to investigate how SSC methods can serve as the main element for managing complex urban data and advancing heritage, socio-economic, and environmental sustainability. The research employs mixed methods such as mapping, serial vision, and walking tools to survey Baghdad’s heritage centre (Old Rusafa) natural and built environment and cultural heritage condition. Together, these methods provide a comprehensive understanding of the heritage area’s physical and socio-cultural dimensions. It is argued that achieving smart urban heritage requires the adoption of sustainable strategies that promote the conservation of architectural heritage. Accordingly, the research outcomes enhance understanding of the smart sustainable city concept (SSC) impact on Baghdad city’s cultural heritage regeneration and allow for the creation of an Index Wheel, which provides city stakeholders with a range of strategies and indicators to conserve Baghdad’s built heritage sustainably. Full article

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. This review examined the role of indoor mapping in understanding, modelling, and reducing GHG emissions in buildings. It synthesized current advancements in indoor spatial data acquisition, ranging from Light Detection And Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM) to deep learning-based floor plan extraction, and evaluated their contribution to improved indoor environmental analysis. The review highlighted emerging techniques, challenges, and gaps, particularly the limited integration of physical indoor spaces with virtual layers representing assets, occupants, and equipment. Addressing this gap requires embedding spatial modelling as an intermediate analytical layer that structures and contextualizes sensor data to support spatiotemporal decision-making. Overall, this review demonstrated that indoor mapping plays a critical role in transforming spatial information into actionable insights, enabling more accurate energy modelling, enhanced real-time building management, and stronger data-driven strategies for GHG mitigation in the built environment. Full article

The nature of nonlinear dynamics of financial markets results in fractal geometry and chaotic behavior that can be viewed on a variety of scales in time. This paper conducts research on the fractal characteristics of the stock network and its contribution to the price of assets based on the Fractal Market Hypothesis (FMH). A multiscale network centrality measure is built based on high-frequency return dependencies to measure the self-similar, scale-invariant nature of inter-stock dependencies. The network factor and portfolio returns are then broken down with the wavelet packet decomposition (WPD) to obtain frequency-domain profiles, which characterize the variability of risk transmission in relation to investment horizons. The profiles are consistent with scaling properties of fractal, but the decomposition does not identify causal pathways on its own. Estimation of fractal dimension by use of the box-counting technique aided by the Hurst exponent analysis reveals that the A-share of China market exhibited long-range dependence and multifractal scaling. Network factor has the largest explanatory power in mid-frequency between the D5 and D6 bands of 32 to 128 days. This intermediary frequency concentration is consistent with the hypothesis of heterogeneous markets, in which the groups of investors with varying time horizons generate scale-related price dynamics. The addition of the network factor to a 6-factor specification lowers the GRS under the 5-factor specification by 31.45 to 17.82 on the same test-asset universe, indicating better cross-sectional coverage in the sample. The estimates of the Lyapunov exponents (0.039) as well as the correlation dimension ($D_2=4.7$) confirm the presence of low-dimensional chaotic processes of the network factor series, but these values are specific to the Chinese A-share market over the 2005–2023 sample period. These results provide a frequency-disaggregated use of network-based factor modeling and suggest that it can be applicable in multiscale portfolio risk management where the investor horizon is not uniform. Full article

The implementation of digital twin (DTw) in infrastructure management is becoming increasingly important. Although digitalization in the Architecture, Engineering, Construction, and Operations (AECO) sector is progressing slowly, enabling technologies such as Building Information Modelling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT) and data management allow for more informed and efficient management of ageing and highly complex assets. With the aim of improving the operation and maintenance (O&M) of transport infrastructure, the use of an integrated BIM–GIS model is proposed as the basis for a future DTw for an existing highway, the M-30 urban ring road in Madrid. This study develops an as-built digital model based on real GIS data, point clouds and BIM (LOD 300), adapting it to existing management systems using a relational database with unique identifiers. The infrastructure is modelled in a segmented and georeferenced manner, incorporating roads, tunnels, bridges and equipment as independent entities. Access to the model is guaranteed through 3D GIS scenes, interactive panels and BIM viewers geared towards management. In addition, a cost–benefit analysis is carried out using a Return On Investment (ROI) that evaluates the implementation of BIM in the management of this infrastructure. Full article

Urbanization and increased traffic across Europe are leading to increased exposure of the population to harmful levels of noise, primarily caused by road traffic. Over 30% of the population is exposed to levels exceeding the limits recommended by the World Health Organization (WHO). Among the various noise reduction strategies, reinforced concrete noise barriers stand out as one of the most effective passive measures. This research analyses the geometric optimization of reinforced concrete noise barriers with different top-edge designs (flat, T-shape, symmetric and asymmetric V-shape) using the Bound Optimization by Quadratic Approximation (BOBYQA) method. The analysis was conducted using COMSOL Multiphysics software, where a coupled solid mechanics and pressure acoustics model was developed. The simulations were performed in a frequency range from 50 to 3150 Hz. The results show that geometry has a significant impact on acoustic efficiency, with the asymmetric V-shape demonstrating the greatest noise reduction. These findings highlight the key role of geometric optimization in the design of cost-effective and sustainable noise protection solutions. Full article

Material circularity in construction requires material information at the end of life for the trading of materials. Different digital technologies (DTs) are essential for such information management. This research aims to review key aspects of developing a blockchain-based material passports (MPs) system when integrating with key DTs used for MPs. This research is based on a critical literature review, with an integrative approach that synthesises both academic and grey literature. The literature search was initiated using chosen keywords relevant to the topic to first identify the key literature. This was followed by using a snowballing technique to expand the search with further relevant literature. Building Information Modelling (BIM), digital twin (DTw) and blockchain technology (BCT) were identified as key technologies for material information management. BIM and DTw are central to the management process as all the information created and collected is modelled, visualised, analysed and stored using BIM platforms. However, existing MP platforms utilising centralised databases to store data were found to be unreliable for managing material data in an industry like construction with a dispersed supply chain and typically longer lifecycle. BCT was realised as necessary for information management in construction, as it allows us to manage information in a more decentralised, transparent and immutable manner. Furthermore, examining current research about blockchain application for information management in construction led to the conclusion that, although the studies on blockchain-based MP platforms covering the entire industry supply chain prevail, the management of material data at the built asset level throughout its lifecycle using such MP systems is underexplored. Thus, building on the literature review, a conceptual model of blockchain-based MP system is proposed in this paper, describing integration with BIM and DTw, and with relevant processes and actors to manage MP information throughout the building lifecycle. Acknowledging the limitations of a subjective literature review, the conceptual model and the ideas are proposed as a foundation for further research and develop MP system with empirical validation. Although theoretically, this study identifies the suitability of blockchain technology for managing product lifecycle information in industry like construction and provides ground for further theoretical research for planning and policy required for blockchain-based MP development and implementation. Full article

Counterintuitively, carbon risks—including investments in net-zero emissions-enabling technologies, legacy assets, insurance costs, and regulatory and compliance expenses—can be managed through rapid decarbonisation, as the built environment sector prepares for a transition to a low-carbon economy. This paper uses a bottom-up approach to net-zero emissions modelling to discuss an accelerated emissions reduction pathway while targeting both net-zero operational and embodied carbon emissions for commercial buildings. It also explores the link between built environment-related policy frameworks and technological advancements aimed at decarbonising commercial buildings, along with an initial effort to improve their energy resilience. For the commercial building archetype, achieving the net-zero operational emissions goal by 2035 appears practical, as energy intensity can be reduced sharply from around 120 kWh/m² to nearly 75 kWh/m² between 2025 and 2035. However, achieving net-zero embodied emissions appears practically challenging, as concurrent policies are at early stages, navigating the embodied carbon emissions data, reporting, and disclosure aspects. Regulatory mechanisms that require the disclosure of both embodied emissions data and actions and progress aligned with the dedicated targets and caps allocated to the real estate sector can assist commercial buildings in delivering on the whole-of-life net-zero emissions targets and commitments. Full article

Real estate constitutes a core segment of the global building and built environment industry, absorbing substantial volumes of international institutional investment capital. Unlisted real estate has featured prominently in the portfolios of global institutional investors. In recent years, global real estate markets have been significantly impacted by rising interest rates, posing a real and significant risk to investors. In response, more tactical asset allocation strategies have been adopted. Investment fund managers and institutional investors seek to rebalance through sector selections and sectoral portfolio diversification when tactical asset allocation strategy may be insufficient in phases of heightened rate volatility. By deploying MSCI US unlisted sector-specific real estate quarterly total returns between March 1999 and June 2024, this research assesses portfolio asset allocation strategy for unlisted sector-specific real estate over both rate-easing and rate-tightening phases to investigate how the structural change shapes portfolio asset allocation strategy resulting from the rising interest rates. Overall, the findings show that unlisted sector-specific real estate played a substantial role in the US institutional mixed-asset portfolios during rate-hike phases in the period before the COVID-19 recession. The allocation to unlisted sector-specific real estate was close to the maximum 10% cap, averaging 9.5% during rate-easing phases but decreasing to 7.5% during rate-tightening phases. At a sector level, unlisted office real estate allocations were higher across constrained mixed-asset and real estate portfolios in rate-tightening phases relative to those in rate-easing phases, while portfolio asset allocations to unlisted real estate sectors were lower in rate-easing phases relative to those in rate-tightening phases. These empirical findings provide real estate investment stakeholders with practical and crucial insights into rebalancing portfolios’ tactical asset allocation strategies for unlisted sector-specific real estate responding to interest rate phases and macro-financial markets, albeit static asset allocation strategies being insufficient in phases of heightened rate volatility. The investment implications of empirical outcomes are identified and further discussed. Full article

Digital Twin technology is transforming how buildings are designed, operated, and optimized, serving as a key enabler of smarter, more energy-efficient, and sustainable built environments. By creating dynamic, data-driven virtual replicas of physical assets, Digital Twins support continuous monitoring, predictive maintenance, and performance optimization across a building’s lifecycle. This paper provides a structured review of current developments and future trends in Digital Twin applications within the building sector, particularly highlighting their contribution to decarbonization, operational efficiency, and performance enhancement. The analysis identifies major challenges, including data accessibility, interoperability among heterogeneous systems, scalability limitations, and cybersecurity concerns. It emphasizes the need for standardized protocols and open data frameworks to ensure seamless integration across Building Management Systems (BMSs), Building Information Models (BIMs), and sensor networks. The paper also discusses policy and regulatory aspects, noting how harmonized standards and targeted incentives can accelerate adoption, particularly in retrofit and renovation projects. Emerging directions include Artificial Intelligence integration for autonomous optimization, alignment with circular economy principles, and coupling with smart grid infrastructures. Overall, realizing the full potential of Digital Twins requires coordinated collaboration among researchers, industry, and policymakers to enhance building performance and advance global decarbonization and urban resilience goals. Full article

The rising usage of IoT devices in everyday life has formed smart cities that require the adoption of decentralized systems for a secure and transparent mechanism to manage asset exchange across automotive supply chains. Several existing Blockchain-based models built on public chains focus on traceability while overlooking scalability limits, transaction fees, conditional payment trust, or real-time delivery validation. We introduce DeFiTrustChain, a DeFi-enabled framework that combines free NFTs, escrow-based automation, and IoT verification within a Hyperledger Fabric network. It represents each vehicle using a unique NFT to capture the details of manufacturing and ownership, along with immutable asset verification. The payment release between stakeholders is governed by a dedicated escrow contract responsible for IoT-based delivery confirmation. The proposed framework ensures authenticated access and prevents identity misuse through integration of the Fabric Certificate Authority. The experimental results demonstrate the coherent and dependable execution of NFT creation, escrow enforcement, and IoT-triggered validation, with low local transaction processing time and consistent behavior across peers. Full article

In response to the increasing demand for effective bridge management and the shortcomings of current proprietary solutions, this work presents an open-source, web-based platform designed to support bridge inspection and data management, particularly for small and medium-sized public administrations, which often lack personnel or funding for implementing context-specific tools. The system addresses fragmented workflows by integrating multi-format geospatial and 3D data—such as point clouds, CAD/BIM models, and georeferenced imagery—within a unified, modular architecture. The platform enables structured inventory, interactive 2D/3D visualization, defect annotation, and role-based user interaction, aligning with FAIR principles and interoperability standards. Built entirely with free and open-source tools, the P.O.N.T.I. prototype ensures scalability, transparency, and adaptability. A multi-layer navigation interface guides users through asset exploration, inspection history, and immersive 3D viewers. Fully documented and publicly available on GitHub, the system allows for deployment across varying institutional contexts. The platform’s design anticipates future developments, including integration with IoT monitoring systems, AI-driven inspection tools, and chatbot interfaces for natural language querying. By overcoming existing proprietary limitations and providing access to a versatile single space, the proposed solution supports decision-makers in the digital transition towards a more accessible, transparent and integrated infrastructure asset management. Full article