The rapid development of the digital economy has led to a significant increase in the scale and electricity load of 5G base stations in urban areas. It is therefore essential to study the load characteristics of 5G base stations to enhance urban grid stability and improve the energy efficiency of telecom operators. This paper investigates the load characteristics of 5G base stations using energy data from 37,525 5G base stations. The comprehensive analysis examines spatial distribution patterns, temporal load characteristics, and grid integration impacts using 15-minute resolution data. Results show that base station deployment is strongly correlated with population density (r = 0.92). Temporal load patterns show strong seasonal characteristics, with summer consumption about 1 kW higher than winter consumption, driven primarily by temperature-dependent cooling demand. The base stations maintain consistently low capacity utilization rates (26-41%), indicating substantial infrastructure redundancy. Strong synchronization exists between base station and grid loads during peak consumption periods, increasing peak stress but also revealing significant demand response potential. This study can help urban energy facilities identify new flexible sources and improve 5G station planning and operations.

Ultra-high performance concrete (UHPC), an advanced cementitious composite characterized by superior mechanical properties and durability, requires multi-objective collaborative optimization in mix proportion design to facilitate its large-scale engineering applications. To address this challenge, this study proposes an intelligent design framework for UHPC driven by the synergistic integration of Stacking ensemble learning and the non-dominated sorting genetic algorithm III (NSGA-III). In the model construction stage, a Stacking ensemble model was developed by integrating eight heterogeneous algorithms as base learners and employing linear regression as the meta-learner. Subsequently, six hyperparameter optimization strategies were employed to fine-tune the model. At the optimization decision stage, the optimal Stacking ensemble model was embedded into the NSGA-III algorithm, and the technique for order preference by similarity to ideal solution (TOPSIS) was employed to select the optimal mix design from the Pareto front. The results demonstrate that the predictive accuracy of the Stacking ensemble model significantly outperforms that of the individual single-algorithm models. Furthermore, multi-objective optimization conducted for three target strength grades, UC100, UC120, and UC140, revealed that all optimized schemes successfully meet the specified strength requirements while effectively balancing environmental impact and production cost. This research establishes a data-driven, multi-criteria intelligent decision-making pathway for the multi-objective design of UHPC, with substantial implications for advancing the interdisciplinary integration of materials science and artificial intelligence.

Ultraviolet germicidal irradiation (UVGI) is widely applied in heating, ventilation, and air-conditioning systems to reduce airborne pathogen transmission, yet its effectiveness depends on airflow-driven changes in lamp thermal condition and particle residence time. This study experimentally characterises the coupled thermal and optical performance of 95 W and 60 W low-pressure mercury ultraviolet-C (UV-C) lamps and uses the measured irradiance fields for comparative germicidal assessment. Air velocities of 1.1-2.5 m/s and ambient temperatures of 14-22 °C were varied to quantify their effects on lamp-surface temperature and 254 nm output. The 95 W lamp maintained near-maximum output over a broader operating range, whereas the 60 W lamp was more sensitive to convective cooling. Reynolds and Rayleigh numbers ranged from approximately 1.24 × 10³ to 2.88 × 10³ and 7.2 × 10³ to 1.3 × 10⁴, respectively, while the convection-to-radiation heat-loss ratio increased with airflow velocity. Mean modelled ultraviolet (UV) dose increased with lamp count and decreased with airflow velocity. At 1.1 m/s, six-lamp arrays delivered mean modelled doses of approximately 191 J/m² for the 95 W lamps and 146 J/m² for the 60 W lamps. Published UV susceptibility constants produced calculated reductions exceeding 6 log for susceptible viral and bacterial species, whereas the calculated reductions for Aspergillus spores ranged from below 0.05 to approximately 0.4 log. These microbial reductions were calculated from the measured irradiance fields and published susceptibility constants and were not experimentally validated using bioaerosols. The results provide a measurement-informed basis for comparing lamp power, airflow condition, and lamp arrangement in in-duct UVGI systems.

The rapid development of the low-altitude economy has positioned vertiports as critical heavy-asset hubs in urban logistics networks, where siting decisions affect both delivery access and facility utilization. Existing maximum-coverage models do not explicitly represent station throughput or allocated order volumes. This study therefore retains the original three-stage framework-demand assessment, hierarchical facility polygon intersection point set (HFPIPS) candidate generation, and capacitated location-allocation, while correcting the demand surface to use area-weighted land-use shares and clarifying that the utilization floor is an application-specific minimum-throughput rule rather than a new algebraic form. In Nanjing, 8,811 demand grids and 10,283 planning candidates produce 2,678,898 radius-feasible pairs, reduced to 374,873 pairs by demand-adaptive pruning. Under a 300 s single-thread budget, the high-coverage capacitated facility location problem (CFLP) returns a verified feasible incumbent of 195 stations delivering 156,300 orders/day (78.13%), with 66.79% average utilization, 22.50% minimum utilization, and 0.953 km average delivery distance. At the same 195-site budget, a deterministic maximal covering location problem (MCLP)-greedy baseline achieves 97.05% nominal geometric coverage but only 63.87% capacity-feasible delivery. Removing the utilization floor delivers only 29 additional orders while opening four extra sites and permitting utilization as low as 1.58%. Land-use coefficient contrast tests change delivery by less than 0.27 percentage points and preserve the selected station set. The results support capacity-aware planning, while building-level engineering data, heterogeneous fleets, temporal demand, and unresolved solver gaps remain limitations.

This study develops a life-cycle techno-economic analysis and optimization framework for hybrid deep ground source heat pump (GSHP) systems, addressing the limitations of existing evaluations that often neglect subsurface thermal attenuation and rely on simplified heat transfer models. A coupled heat transfer model is established by combining the finite difference method for the borehole interior with the segmented finite line-source model for the surrounding ground, enabling long-term simulation of fluid and ground temperature evolution. The energy model is integrated with a life-cycle economic evaluation to calculate net present value, levelized cost of heating/cooling, payback period (PP), and internal rate of return. A Bayesian optimization algorithm is further employed to identify optimal system configurations under multiple objectives. The framework is applied to a hybrid geothermal system in Xi’an, China, incorporating mid-deep boreholes with auxiliary air-source heat pumps or gas boilers. Results show that long-term thermal attenuation significantly affects economic performance, with a 2 °C increase in fluid temperature extending the PP by approximately 0.3 years. User-side load uncertainty also has a substantial impact, as a 10% deviation from the design load can shift the optimal PP by about 2 years. Sensitivity analysis identifies the GSHP installed capacity ratio and borehole number as the most influential parameters. Compared with pure geothermal systems, the hybrid geothermal–gas boiler configuration achieves the shortest PP, demonstrating the framework’s effectiveness for robust low-carbon system design.

Alkali-activated lunar regolith is considered one of the most promising lunar regolith-based construction materials for large-scale lunar construction. This study presents a comprehensive framework integrating machine learning (ML) algorithms to investigate the influence of various features on the mechanical strength of alkali-activated lunar regolith simulant (AALRS), aiming to achieve the strength prediction and optimization design of AALRS. The properties of lunar regolith simulant, mixture proportions, preparation parameters, environmental conditions, and enhancement methods were employed as input features for ML modeling. The compressive and flexural strength predictive models were constructed using eight ML algorithms and evaluated through statistical indicators. Among the models, Extreme Gradient Boosting demonstrated the best performance, yielding an R² of 0.8684, a root mean square error of 6.2007 MPa, and a mean absolute error of 4.0874 MPa on the testing dataset. Using the best prediction models, four design strategies with three objectives, namely, compressive strength, flexural strength, and transport payload (TP), were optimized and evaluated using the non-dominated sorting genetic algorithm II and the technique for order preference by similarity to ideal solution methods. The proposed prediction and optimization framework for the mechanical performance of AALRS, which integrates extreme environmental effects and TP-driven design, provides a robust data-driven approach for the design, prediction, and optimization of AALRS, advancing the development of extraterrestrial construction materials and technologies.

Open-water drowning is a leading serious-injury/fatality risk at public waterfronts and in construction over or adjacent to water, where long stand-off views, glare, waves, and occlusions hinder timely detection. We propose TimeSformer+MIL, a weakly supervised temporal framework for event-level drowning monitoring designed for deployment in safety-critical construction settings and aligned with supervisory workflows. The system standardizes video streams into short clips, extracts spatiotemporal evidence with a divided space–time TimeSformer, and aggregates clip scores via top-k multiple-instance learning with a lightweight consistency prior to stabilize weak labels and support calibration. By avoiding person detection and multi-object tracking, the pipeline reduces engineering complexity and failure modes common in cluttered, low-light, or small-scale scenes, improving reliability without increasing operator load. We curate an open-water dataset spanning construction and public-waterfront contexts and evaluate with event-focused metrics aligned to risk governance: recall at target false-positives per hour, alert latency relative to rescue windows, and calibration-aware ranking and thresholded decisions for alerting and escalation. Across clip lengths and aggregation strategies, the approach delivers robust discrimination and translates it into stable, low-latency alerts that meet rescue-time targets while limiting alarm fatigue. For architectural practice and construction safety management, the framework offers a practical path to augment human surveillance with machine attention, functioning as a verifiable administrative control within the hierarchy of controls and integrating with site safety processes to accelerate incident recognition and strengthen risk governance in dynamic open-water settings.

Thermal comfort, indoor air quality, lighting comfort, and acoustic comfort are often treated as separate domains, despite interacting trade-offs. The discrete choice experiment (DCE) technique helps to account for explicit and implicit factors influencing choices, giving a more holistic view of indoor comfort. This paper reviews discrete choice experiment studies that include indoor environmental comfort elements. From 1,418 records found in scientific databases, 21 studies were reviewed. By analysing these studies, DCE’s ability to value indoor comfort was evaluated and methodological deficiencies identified. Given the heterogeneity between studies, this review focused on analysing the p-value and the direction of estimates. Thermal comfort in these studies shows positive and statistically significant coefficients, indoor air quality is generally positive but statistically heterogeneous. Evidence for positively valued lighting and acoustic comfort is frequently statistically non-significant. Methodological challenges identified affecting indoor environmental quality (IEQ) valuation in DCEs include proxy dilution, insufficient attribute clarity, embedded cost signals, and demographic heterogeneity. Proxy-based operationalisations may confound comfort valuation with convenience, cost, or multi-domain interaction effects. Additionally, occupant control and agency emerge as meta-attributes that generate utility beyond environmental improvement. Survey-based DCE can reliably elicit thermal comfort preferences from occupants; experiential approaches such as virtual reality-based DCE may allow occupants to better value acoustic and lighting comfort. Overall findings suggest indoor comfort should be modelled as a multi-attribute bundle instead of separate domains. This review shows that DCE is a useful tool for analysing indoor comfort as a bundle of attributes with strong interaction effects.

Construction jobs involve heavy, repetitive loads and awkward postures that are prone to increased risk of musculoskeletal injury. As a result, a recognized demand exists for dependable exoskeleton support to improve workers’ safety and productivity. The main objective of this project was to enhance the controls based on the ability of surface electromyography (sEMG) to obtain the motion intentions of its users, preventing mistimed torque and excessive force. To solve the issues of signal noise and inter-individual variability, this study proposes a hybrid model, which combines nonnegative matrix factorization to extract transferable muscle coordination patterns, a convolutional neural network to learn robust local cross-channel features, and a Transformer encoder to capture long-range temporal dependencies. This approach was tested on a bricklaying dataset that mimics the actual conditions on construction jobsites. The model performance was 87% in action-recognition accuracy, and generalized across subjects, illustrating robustness to signal artifacts along with differences in tools, postures, and workloads. The results support the proposal of sEMG as a non-invasive practical control signal for exoskeletons and indicate that the proposed model can enable timely and precise assistance on a variety of tasks. This study extends the previous research in intent recognition for field-ready exoskeleton manipulation and establishes the foundation for future multi-task adaptation and personalized assistance in the construction process.

Developing new quality productive forces (NQPFs) in the building and real estate industry is essential for sustainable economic growth. Utilizing panel data on Chinese A-share listed building and real estate companies from 2011 to 2024, this study empirically investigates the impact of digital transformation (DT) on corporate NQPFs and the underlying mechanisms. Our baseline findings confirm that DT exerts a significantly positive effect on NQPFs, effectively overcoming the “Solow Paradox” in this capital-intensive sector. Moving beyond this initial assessment, the study systematically unpacks the mechanisms underlying digital value creation. We reveal that DT promotes the enhancement of NQPFs through three core mechanisms: fostering technological innovation, upgrading the human capital structure, and improving asset operational efficiency. Furthermore, the multidimensional heterogeneity analysis demonstrates that this digital empowerment is highly context-dependent. The enabling effect is significantly more pronounced in the Eastern and Central regions and is amplified by stronger intellectual property protection, more advanced digital infrastructure, state ownership, and larger firm size. By elucidating these specific pathways, this research offers targeted guidelines for sector-specific digital policy-making and strategies to optimize corporate NQPFs.

The construction industry is undergoing a low-carbon and digital transformation. The application of recycled aggregates in 3D printed concrete has emerged accordingly, combining the advantages of solid waste recycling and automated manufacturing. However, the optimization of mix design and the prediction of mechanical properties for 3D printed recycled aggregate concrete (3DPRAC) face two main challenges, namely the micro defects inherent in recycled aggregates and the anisotropy caused by layered deposition. This study proposes a Gene Expression Programming (GEP)-based computational framework for the prediction of the splitting tensile strength (STS) of 3DPRAC. A dataset of 110 data points was generated, with layer height and loading direction as key printing parameters representing the anisotropic effect. GEP showed the best performance among models, with an R² of 0.914 on the testing set. Furthermore, the GEP model provides explicit mathematical equations that delineate the contributions of individual input variables and reveal the nonlinear effects of fiber content and printing parameters on STS.

This study addresses the interoperability of building information modeling (BIM) across different systems and platforms. Because the semantics of the industry foundation classes (IFC) standard are large and complex, traditional full semantic conversion methods are general-purpose, but they often lead to data expansion, which reduces system responsiveness and usability. In addition, the strong dependence of BIM models on specialized software further limits flexibility in practical use. Our novel framework combines a streamlined ontology with IFC, significantly improving memory efficiency and operational effectiveness compared to existing systems. Additionally, we integrate large language models for enhanced natural language processing in BIM data interactions. This harmonization of technologies not only simplifies system extension but also makes BIM data services more user-friendly and adaptable to various industry needs. By streamlining BIM data management and enriching data services, our approach broadens BIM’s applicability and improves data integration and extraction, establishing a more interactive and user-centric paradigm.

Background: Knowledge graphs (KGs) and Semantic web technologies (SWT) are increasingly explored in facilities management (FM) to address persistent problems of interoperability, fragmented data, and limited automation across operational systems.

Background: Knowledge graphs (KGs) and Semantic web technologies (SWT) are increasingly explored in facilities management (FM) to address persistent problems of interoperability, fragmented data, and limited automation across operational systems.

Methods: A scoping review was conducted in accordance with PRISMA-ScR 2020 to map KG applications in FM and identify emerging research directions. Web of Science and Scopus were queried for English-language, peer-reviewed studies applying ontologies, SWT, linked data, or KGs at building-level FM. Studies focused on non-FM contexts, earlier lifecycle phases without operation and maintenance relevance, or artificial intelligence and machine learning approaches without explicit SWT/KG integration were excluded. Following de-duplication and qualitative screening, 48 studies were thematically analysed.

Results: Four dominant application areas were identified: (1) ontology-driven semantic interoperability (n = 36), (2) systems integration across FM data sources (n = 24), (3) data and information retrieval from graph-based stores (n = 14), and (4) automated compliance and validation through knowledge-based reasoning (n = 6). Most studies employed modular, domain-specific ontologies deployed on graph databases to link static building information modelling and asset data with near real-time streams. Emerging directions include the use of KGs as semantic integration layers in digital twin (DT) architectures, and KG-grounded retrieval-augmented generation to enhance trustworthy and explainable access to FM information.

Conclusion: This review consolidates dispersed research on KG applications in FM and outlines a structured research agenda spanning modular ontology development, integration with DT and compliance workflows, as well as longer-term opportunities for scalable and auditable generative AI applications. Findings reflect peer-reviewed journal literature and may under-represent emerging industry practice.

This study addresses the challenges of solidifying high-moisture, high-alkalinity inorganic softening sludge (ISS) by utilizing alkali-activated slag-based cementitious materials. The research investigated strength evolution via unconfined compressive strength tests, while X-ray diffraction and scanning electron microscopy were employed to analyze phase composition and microstructural changes. Furthermore, response surface methodology was utilized to optimize mix proportions and explore interaction mechanisms. Results indicate that granulated blast furnace slag is the primary strength contributor, whereas fly ash and sodium silicate exhibit nonlinear effects. Microscopic analysis reveals that changes in raw material ratios influence strength development by regulating the formation of products such as calcium aluminosilicate hydrate (C-A-S-H) and sodium aluminosilicate hydrate (N-A-S-H). Optimization results from response surface methodology show that, when sodium hydroxide dosage is fixed at 2.5%, the optimal dosages for granulated blast furnace slag, fly ash, and sodium silicate are 30.65%, 29.98%, and 6.96%, respectively, achieving a 7-day unconfined compressive strength of 5.38 MPa. Furthermore, significant interactions exist between granulated blast furnace slag and fly ash, as well as between fly ash and sodium silicate. This study provides a theoretical basis for the resource utilization of ISS in road base materials.

With global warming, urban workers engaged in physically demanding occupations are increasingly exposed to severe heat stress. While the physical health risks are well recognized, less is known about how heat stress affects cognitive function at the neurophysiological level, an understanding critical for protecting worker well-being. This study conducted a controlled laboratory experiment to examine cognitive and neural responses to heat stress in middle-aged heat-exposed manual workers. Twenty participants (mid-40s) completed six representative cognitive tasks under four wet-bulb globe temperature (WBGT) conditions (23, 26, 28.5, and 31 °C). Electroencephalogram (EEG) recordings were used to monitor brain activity and assess changes in relative band power across δ, θ, α, and β frequency bands. The results revealed that performance in the executive- memory dimension peaked under moderate heat (forming an inverted-U pattern), while performance in the sensorimotor reactivity dimension remained stable or improved with rising WBGT. Neurophysiologically, rising heat stress led to increased δ band power but decreased α, θ, and β band powers. These EEG band power changes showed a nonlinear relationship with cognitive performance across both dimensions, with the left frontal cortex demonstrating higher sensitivity. Furthermore, topographical coupling maps indicated that executive-memory demanding tasks activated a more widespread cortical region than sensorimotor-reactivity demanding tasks. These findings show that graded heat stress alters brain dynamics and cognitive performance in a cognitive load-dependent manner, offering insights for designing adaptive work environments and real-time cognitive performance monitoring devices for heat- exposed workers.

While digitalization is fundamentally reshaping circular construction, the specific evolutionary trajectory from static building information modeling (BIM) toward dynamic Digital Twins (DT) remains unclear. To unpack this complexity, a PRISMA-guided systematic review and scientometric mapping of 271 publications (2019-2025) was performed using VOSviewer and CiteSpace. The analysis identifies a four-phase pathway: (i) strategic foundations focused on circular frameworks and material reuse; (ii) design integration emphasizing tool implementation and BIM workflows; (iii) dynamic integration toward digital twins enabling lifecycle data continuity; and (iv) systemic management and lifecycle assessment for real-time control and compliance. Critical turning points in this journey are marked by burst terms such as “framework” (2021), “integration” (2022), and the current emphasis on “management” (2023-2025). Crucially, the review synthesizes conflicting paradigms to identify three fundamental gaps hindering the BIM-to-DT shift: the ontological schism in data sufficiency, the agency gap between digital shadows and twins, and the logistical gap in stochastic reverse flows. To bridge these divides, a three-layer architecture is proposed in which BIM serves as the static product backbone, process information modelling as the essential logic bridge, and DT as the operational decision core. This constructs a time-indexed evolution model that quantifies the shift from process optimization to systemic value creation and provides an adoption roadmap prioritizing interoperability to support policy instruments.

The application of sustainable manufactured sand (MS) in 3D printed concrete is currently severely restricted by the “pumpability-buildability” conflict, primarily characterized by the high internal friction and angularity of MS particles. To overcome this bottleneck, this study proposes a novel process-oriented strategy: a secondary mixing protocol featuring a static resting period and delayed superplasticizer addition. This approach is explicitly designed to induce a “rheological rejuvenation” effect in stiff MS mixtures. Quantitative results demonstrate that this protocol reduces the static yield stress of high-volume MS ink (T-3) by approximately 39% (from 1,516 Pa to 924 Pa) and significantly diminishes thixotropic hysteresis, thereby successfully reopening the effective printing window to approximately 65 min. Although increasing MS content accelerates the structural build-up rate, the optimized 3D printed manufactured sand concrete (T-3) achieves a 28-day compressive strength of 61 MPa, representing a 15% enhancement over the river sand reference group (T-0). These findings confirm that the secondary mixing protocol is a robust solution capable of unlocking the potential of high-volume MS in sustainable digital construction.

Hazard-adjacent sites such as seawalls, bridge edges, quaysides, and fenced industrial perimeters routinely require metric positioning without in-zone fiducials, survey control, or repeated traversal, yet still demand decision-ready uncertainty on a site-meaningful datum. We present a datum-constrained forward intersection (DFI) pipeline that completes metric anchoring in a reachable safe subregion and transfers the uncertainty of scale, pose, and datum plane across the boundary to a targetless zone. In the safe-zone calibration stage, standard ChArUco imagery drives a prior-constrained bundle adjustment that jointly estimates multi-camera extrinsics and the site datum plane under weak but auditable tape-measure priors (inter-camera baseline and optical-center heights). In the target-zone measurement stage, undistorted pixel rays intersect the datum in closed form and are fused by incidence weighting; first-order propagation yields a 95% confidence region on the datum plane for each reported coordinate and distance. To tolerate consumer-grade instabilities (modest refocus/zoom and mixed resolutions), the pipeline supports BA-anchored refinement and a minimalist one-parameter in-situ metric correction (DFI–MScale) based on a few taped distances. In the test pair, DFI–MScale attains a median/p90 distance error of 62/106 mm, reducing p90 by 88.5% versus ORB, 87.6% versus LoFTR, and 78.4% versus RoMa under identical intrinsics and target points.

Seawater-sea sand concrete has emerged as an innovative construction material due to its effective utilization of marine resources. At the same time, geopolymer concrete has shown rapid hardening capabilities, low energy consumption, and eco-friendly properties, making it highly promising for structural reinforcement. This study evaluated the effects of alkali modulus, alkali concentration, and fly ash (FA) content on the performance of geopolymer FA-slag seawater-sea sand mortar (SWSSGM). Performance characteristics such as consistency, flowability, compressive strength, and flexural strength were tested. The findings indicate that FA enhances workability, whereas slag powder has the opposite effect. Increasing alkali concentration reduces consistency but increases slump. Optimal compressive and flexural strength is achieved when the alkali modulus is 1.2, alkali content is 15%, and FA content is 30%. Generally, higher alkali concentrations promote strength development, while increased FA content raises porosity and reduces strength. However, strength gains over time are more pronounced, and the effects of alkali modulus require further investigation. Multiple linear regression analysis of the three factors affecting SWSSGM properties demonstrates that a quadratic polynomial regression model yields high coefficients of determination (R² ≥ 0.92), enabling accurate predictions of SWSSGM performance parameters. From a lifecycle (cradle-to-gate) perspective, SWSSGM outperforms traditional cement-based materials in terms of carbon emissions, energy consumption, and resource efficiency, showcasing its significant low-carbon, energy-saving, and environmentally friendly attributes. Through systematic optimization of SWSSGM’s design parameters, performance prediction modeling, and preliminary environmental assessment, this study provides guidance for its sustainable application in coastal engineering projects.

The increasing digitalization of the construction industry is reshaping how professionals are trained and educated. As a key component of construction education, traditional site visits provide students with valuable first-hand exposure to real-world construction environments. However, despite their educational value, such visits present multiple challenges, including scheduling difficulties, limited site capacity, health and safety restrictions, induction requirements, and high logistical costs. In recent years, Virtual Reality (VR) has offered new opportunities to deliver remote, realistic, and interactive site experiences that replicate the authenticity of on-site learning without requiring physical presence. Previous studies have primarily focused on fully computer-generated VR environments developed using platforms such as Unity or Unreal Engine. While these models offer flexibility and interactivity, they often lack the contextual realism of actual construction sites. Addressing this gap, the present study designed and developed a VR immersive construction site experience tailored to the Australian construction industry, built from 111 high-resolution 360° panoramas captured at a real construction site. The system architecture was developed using an incremental development approach to manage complexity, refine functions, and ensure continuous feedback. Drawing on insights from a systematic literature review, the system architecture specifically targeted barriers to VR adoption in construction education. The resulting prototype was evaluated by 31 students, revealing positive perceptions across all key constructs, with mean scores of 4.31 for usefulness, 4.30 for ease of use, and 4.28 for learning impact (five-point Likert scale). This study represents a pioneer exploration in establishing a scalable and educationally aligned framework for VR-based construction learning. Future development stages will focus on expanding the system to cover diverse project types, integrating adaptive learning features, and linking it with learning management systems to enhance engagement and learning analytics.

Integrating Carbon Capture, Utilization, and Storage technology into concrete additive manufacturing represents an innovative pathway for achieving carbon emission reduction in the construction industry, while simultaneously offering effective solutions to critical challenges such as insufficient early-age strength and weak interlayer bonding in printed components. Bibliometric analysis reveals that this interdisciplinary field is experiencing a period of exponential growth, with research hotspots rapidly shifting from fundamental 3D printing process exploration to the synergistic optimization of material properties utilizing accelerated carbonation and carbon dioxide sequestration technologies. This review systematically elucidates the mechanisms of carbon mineralization curing, with a particular focus on analyzing three key process pathways: fresh state carbon mineralization, synchronous carbon sequestration during printing, and post-printing carbonation curing. Furthermore, the synergistic enhancement mechanisms of material strategies, including supplementary cementitious materials and carbonated recycled aggregates, are discussed. On this basis, the review identifies core challenges regarding curing uniformity, narrow process windows, and the absence of long-term durability research, and subsequently outlines future development directions such as intelligent closed-loop control and integrated structure-material-process design.

Against the backdrop of global climate anomalies and rapid urbanization, rainfall-induced urban flooding has become increasingly frequent. Wuxi, a city in southern Jiangsu Province, suffers annual casualties and economic losses due to flooding, leading to urban chaos. This study proposes a quantitative evaluation of the level of urban flood resilience using a system of resilience evaluation indicators. An evaluation indicator system for urban flood resilience encompassing infrastructure, environment, society, and economy was constructed with 26 indicators. A hybrid multi-criteria decision-making method integrating the Analytic Hierarchy Process, entropy weight method, and GIS technology was developed to assess flood resilience in Wuxi systematically. Additionally, emergy analysis was applied to evaluate urban sustainability. The results indicate that five administrative districts centered in Liangxi District and parts of Yixing City near water bodies exhibit low resilience due to gentle slopes, proximity to active river channels, and high population density. In contrast, most areas of Jiangyin City demonstrate higher resilience owing to elevated terrain, fewer social vulnerabilities, and advanced economic development. The model’s consistency was validated using the Area Under the Curve (AUC) method, yielding an AUC of 0.843. The Emergy Sustainability Index for Wuxi was 0.08 < 1, indicating an unsustainable state. This study provides actionable recommendations for enhancing urban flood management and regional resilience.

Solid waste generation from industrial production and construction activities has increased sharply in recent years. Improper disposal and abandonment of solid waste have led to significant resource loss and pose serious risks to soil, water, and air quality. As a result, incorporating solid waste into concrete production presents a promising strategy to reduce environmental pressure and promote sustainable development. This review investigates the feasibility and performance of various solid waste materials, such as fly ash, limestone powder, stone sludge, silica fume, waste glass, rubber particles, and recycled concrete sand, that are incorporated into 3D printed concrete (3DPC). These materials demonstrate the potential to enhance rheological behavior, mechanical strength, interfacial bonding, and dimensional stability of printable composites. Additionally, their incorporation reduces carbon emissions, diverts waste from landfills, and promotes resource circularity. The benefits and limitations of solid waste are critically evaluated, including printability challenges arising from particle morphology and water absorption. The review emphasizes the need for standardized cost-effectiveness analysis frameworks to quantify economic and environmental balance. Finally, this study highlights the importance of optimizing mix design, surface treatment techniques, and large-scale production strategies to enable widespread adoption of waste-based 3DPC in sustainable construction.

It has been extensively shown that Building Information Modelling (BIM) improves project performance, which is impacted by various critical performance factors of BIM project (CPFs-BP). However, it has become a difficult problem in theory and practice to determine the most important CPFs-BP impacting BIM project performance (BPP) at various life-cycle stages. In light of this information gap, the goal of this study is to identify the most important CPFs-BP and identify the stages at which BIM can be applied to enhance BPP through specific CPFs-BP. Using a review of the literature, this work first discovers and characterizes 17 CPFs-BP. Then, by constructing a two-dimensional, two-mode network of heterogeneous nodes, this study demonstrates the top seven CPFs-BP, the relationship between CPFs-BP and life-cycle stages, and the relative significance of various life-cycle stages in BIM projects using descriptive methods and complex network theory. BPP prediction models were then constructed and validated utilizing fitness parameters using MATLAB and data obtained from expert interviews and surveys. Lastly, five specific strategies are put forth to enhance BPP: increasing BIM accuracy during the construction phase, enhancing stakeholders’ capacity to coordinate the design of construction documents, enhancing BIM accuracy during the completion phase, fostering cooperation between conceptual design organizations, and utilizing the BIM system environment during the construction phase. Important BIM project stakeholders can apply the study’s conclusions. Finally, five specific approaches to improving BPP are proposed: improving BIM accuracy at the construction stage, stakeholders’ ability to organize construction document design, BIM accuracy at the completion stage, collaboration between conceptual design organizations, and the BIM system environment during the construction stage. The study is useful for key stakeholders in BIM projects who can put its findings into practice and help evaluate BPP throughout the project’s life cycle.

Several EU policy and legislative initiatives aim to tackle the problem of major resource use and waste within the built environment, both energy and materials, including plans to decarbonise the sector and implement circularity principles. The use of modular systems in deep energy retrofit is argued to present an accelerated decarbonisation pathway with potential to implement advanced circularity. However, modular retrofit and the integration of circularity in the built environment and its assessment is an emerging area of research with the literature identifying need for comprehensive assessment methods demonstrated in practical case studies. This paper examines the development of a novel holistic design for disassembly assessment framework and method piloted in the prototype design of modular wall over-cladding systems in the deep energy retrofit of social housing in Ireland under the EU Drive 0 project. The findings indicate significant differences in design for disassembly performance between the modular circular solution and a conventional solution, with differences in performances at all construction levels. The method provides a holistic and detailed assessment but is critiqued in terms of complexity and user subjectivity relating to the assessment stage, data availability and professional knowledge and/or experience, with a revised adapted method proposed. The research contributes to knowledge in highlighting the potential and complexity of achieving advanced design for disassembly in practice, demonstrating that there are significant differences in the performances across construction types and hierarchy and that assessment methods need to be robust, holistic, sufficiently detailed but not overly complex for application in practice.

3D-printed concrete (3DPC) represents a rapidly developing technology with numerous applications in construction. The properties of 3DPC under both static and dynamic loads can be enhanced by incorporating reinforcement. This study presents a novel reinforcement technique utilizing U-shaped steel wire mesh (U-shape SWM). This technique involves integrating both horizontal and vertical reinforcements during the concrete printing process. Additionally, a new fabrication process was developed using a dual-arm robotic system, with each arm equipped with shaping and welding tools, to autonomously create U-shape SWM structures. An experimental investigation was conducted on specimens reinforced with either flat or U-shape SWM reinforcements under flexural loading. Compared to the specimens reinforced with a single layer of 6-mm flat SWM under a shear-span ratio of 2, those incorporating one layer of U-shape SWM combined with two layers of 6-mm flat mesh exhibited a 200% increase in failure load and a 26.09% increase in cracking load, while the ultimate displacement decreased by approximately 6%. Under a reduced shear-span ratio of 0.5, this reinforcement configuration reached a failure load of 43.6 kN, a cracking load of 16.1 kN, and an ultimate displacement of 11.1 mm. Furthermore, an analytical model based on the strut-tie model was developed to predict the load-bearing capacity of the reinforced specimens. The model predictions showed good correspondence with the experimental results.

Indoor geometric quality inspection plays a crucial role in construction quality control. Light detection and ranging (LiDAR) can obtain point cloud data of the indoor environments in full range, which then can be utilized to efficiently extract geometric features of building elements for inspection. However, existing data collection of indoor environments using LiDAR is still manually planned and implemented, which is quite time-consuming as the scanning space is usually unknown and the movability of equipment is limited. Therefore, this paper proposes an automated approach for indoor geometric quality inspection data collection based on Building Information Model (BIM) and quadruped robot equipped with LiDAR. First, BIM with accurate geometric and semantic information of building is integrated with heuristic algorithm to automate scan planning, including scan stations and order. Second, Simultaneous Localization and Mapping (SLAM) integrates with BIM is equipped into a quadruped robot to achieve automated data collection capability. Finally, an intelligent execution module for scanning point cloud data in indoor environments is introduced. The proposed approach is validated in real indoor environments. Compared with traditional data collection methods, the experiment results show that the proposed approach can save 42% of scanning time, and the scanned point cloud data has better quality and enough density, which significantly improves the efficiency of inspection data collection for indoor geometric quality management. By unifying BIM-driven planning, SLAM-based localization, and quadruped robot mobility into a single framework, this study introduces a novel approach for large-scale automated indoor inspection, with attention to connected areas to ensure continuity across multiple spaces.

The growing complexity of construction projects demands decision processes that can integrate diverse professional perspectives across design, cost control, construction, and acceptance. Traditional sequential management approaches often leave conflicts unresolved until late stages, causing delays, cost overruns, and rework. To address this issue, this study develops a cross-stage workflow for construction projects that embeds artificial intelligence into collaborative decision-making. The workflow employs digital agents representing different roles, which engage in structured debates supported by evidence from building information modeling models, engineering drawings, cost records, and technical standards. Through iterative exchanges, alternative solutions are proposed, challenged, and refined, with the process producing decisions that are transparent, traceable, and adaptable to changing project conditions. Results from four representative scenarios confirm significant improvements over conventional practice: unresolved design clashes were reduced by more than 40%, cost forecast deviations narrowed from around 12% to below 5%, schedule variance under disruption decreased by about 18%, and first-pass acceptance rates increased from 74% to 88% with fewer reworks. A longer-term case study further demonstrated reductions in cost variance and project duration, together with higher stakeholder satisfaction. By coupling AI-enabled debate mechanisms with established digital construction environments, the workflow turns disciplinary conflicts into structured reasoning that enhances decision quality. The findings highlight how intelligent and collaborative approaches can deliver measurable gains in cost, time, and quality, while offering a practical pathway for advancing smart construction practices.

Effective scheduling in construction projects increasingly depends on allocating scarce multi-skilled labour under strict precedence and capacity constraints. Reinforcement learning (RL) techniques have presented outstanding performances in addressing Resource Constrained Project Scheduling Problem (RCPSP) instances. However, studies are lacking that utilise RL algorithms in solving extended RCPSP like the Multi-Skilled RCPSP (MSRCPSP). MSRCPSP is a nondeterministic polynomial time (NP)-hard problem that enables activity start times and allocation of multi-skilled resources to be determined simultaneously. Unlike the classical RCPSP, each activity specifies explicit skill requirements rather than anonymous capacity units. This study formulates scheduling as a Markov decision process and compares five optimisation agents, including the genetic algorithm, particle swarm optimisation, black-winged kite algorithm, deep Q-Network (DQN), and proximal policy optimisation (PPO), on benchmark instances from the MSLIB library. The results showed that solutions produced by PPO and DQN concentrate near the reference optima. PPO, in particular, attains the largest number of optimal or near-optimal schedules and the shortest makespans. In several instances, the deep reinforcement learning (DRL) agents also outperform the benchmark solutions, plausibly because CPLEX, constrained by a 60-second time limit, returns suboptimal solutions. Overall, the comparative results provide a practical benchmark of widely used heuristics and metaheuristics against DRL baselines, offering guidance for future algorithm selection and hybridisation for MSRCPSP.

Buildings account for a substantial proportion of urban energy demand, making it essential to understand the interrelationships between the built environment, urban heat island (UHI) effects, and energy demand. This study investigates the impacts of UHI on urban building energy demand in Mumbai, India, using a multi-scale framework. First, UHI intensity is assessed by generating and analyzing Land Surface Temperature maps and calculating the Urban Thermal Comfort and Vulnerability Index. This assessment identifies UHI hotspots and regions with potential thermal discomfort. Subsequently, energy demand modelling is conducted across two locations with distinct thermal comfort conditions, spanning from the individual building to the urban scale. Detailed building information and site-specific climate data from the two contrasting locations are collected to support urban building energy modelling (UBEM). Results reveal that UHI increases cooling energy demand by 7.31% at the individual building level; however, its impact on urban-scale energy demand is significantly mitigated by building geometry and surrounding structures. Specifically, variations in building height and inter-building shading outweigh the influence of UHI, leading to a substantial 15.9% reduction in the mean cooling energy demand intensity. These findings highlight the critical role of urban morphology and density in shaping cooling energy demand under UHI conditions. By extending beyond individual building-level assessments to UBEM, the study contributes new evidence regarding UHI-energy interactions in a year-round warm tropical context such as Mumbai. Furthermore, the results position urban-scale cooling energy demand assessments as a transferable framework to integrate UHI research with energy policy, ultimately supporting climate-responsive planning.