Search Results (417)

Low-rise terraced housing constitutes a major segment of Taiwan’s residential market, yet stakeholder perception differences during residential development planning remain insufficiently understood, particularly regarding sustainability considerations. In this study, we investigate cognitive gaps among developers, homebuyers, and construction professionals across six planning dimensions, including site selection, housing price, capital capacity, construction risk, building planning, and sustainability. A structured questionnaire survey was conducted in Changhua County, Taiwan, yielding 176 valid responses (37 developers, 92 homebuyers, and 47 construction professionals). Data were analyzed using Cronbach’s α reliability analysis, exploratory factor analysis (EFA), chi-square tests, one-way ANOVA, Fisher’s LSD post hoc comparisons, and robustness analyses using ANCOVA and Tukey’s HSD. Significant stakeholder perception differences were identified for 15 of the 19 planning factors (p < 0.05). Supply-side stakeholders consistently prioritized construction cost, financing capacity, and construction risk, whereas homebuyers placed greater emphasis on transportation convenience, living amenities, spatial quality, and sustainability-related attributes, particularly green building certification and energy efficiency. Construction risk exhibited the largest cognitive gaps, with large effect sizes for construction difficulty (η² = 0.450) and government regulation (η² = 0.454). Within the sustainability dimension, governance transparency remained non-significant, suggesting that governance awareness has not yet matured into a differentiated stakeholder concern. Based on these findings, we propose the Stakeholder Cognitive Gap Framework (SCGF) as a conceptual and diagnostic framework for organizing stakeholder perception patterns. The findings contribute to understanding stakeholder cognitive divergence in residential development planning and provide practical implications for sustainable housing policy, developer decision-making, and participatory planning in non-metropolitan housing markets. Full article

A major challenge in the optimization of thermal performance of opaque envelope components is that the calculation workload of energy consumption simulations of full factorial combinations leads to a prohibitive increase as the factors and levels multiply. Nevertheless, a reliable method for designing calculation schemes to reduce the calculation workload without sacrificing the accuracy of the results is still lacking. Accordingly, a building energy consumption simulation scheme (BECSS) based on a uniform design method (UDM) was proposed, and its feasibility was verified. A multiobjective optimization model (MOM) with the life cycle cost (LCC) and life cycle carbon emission (LCCE) as optimization objectives was established, and it was solved using the non-dominated sorting genetic algorithm-II (NSGA-II). Furthermore, an entropy-based TOPSIS method was introduced to calculate the optimal insulation thickness (OIT) of opaque building envelopes in severe cold and cold zones. The proposed framework was demonstrated through a case study of a typical residential building in Xuzhou. Extruded polystyrene panels (XPS) were used as insulation materials, and coal-fired boiler (CFB), gas-fired boiler (GFB), and air source heat pump (ASHP) were used as heat sources. Compared with the results specified by the current energy conservation standards, the MOM could achieve a balance between energy savings and environmental benefits. The LCCs change by −3.16–11.34%; nevertheless, the thermal performance of the external wall improves by 42.32–49.31%, while that of the roof improves by 6.46–21.59%; the building energy consumption (BEC) levels and the LCCEs are reduced by 25.24–30.16% and 19.25–24.74%, respectively. The indicator weights determined via the entropy weight method underscore the necessity of incorporating environmental performance indicators in the optimization of thermal performance of opaque building envelope components. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article

The construction sector accounts for 34% of global energy-related CO₂ emissions, yet existing Digital Twin (DT) applications in construction remain largely descriptive, lacking the predictive and prescriptive capabilities required for proactive execution management. This study presents and evaluates, as a simulation-based proof of concept, a risk-informed DT framework integrating a formalized DT state transition model, a dual deep learning prediction engine (Long Short-Term Memory (LSTM) and Transformer), Monte Carlo-based probabilistic carbon quantification for lifecycle modules A1–A5 (S = 10,000), and a risk-adjusted, Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimizer incorporating carbon variance, within a closed weekly feedback loop. The framework is evaluated across five European case studies (residential, education, and commercial office; the Netherlands, the UK, and France) anchored to published project data, with primary calibration on a hybrid real–synthetic mid-rise residential building (CS1; 7200 m², eight stories, the Netherlands) and a fully documented synthetic layer generating the execution records that the published sources do not provide. Under common random number simulation with 50 execution realizations per case, the full framework achieved simultaneous improvements of 5.8–13.2% in expected schedule duration and 10.4–16.4% in expected embodied carbon relative to a static Critical Path Method (CPM) baseline; for CS1, mean intensity fell from 476 to 426 kgCO₂e/m². Component ablation identified the bi-objective optimizer as the dominant contributor, producing an 11.7–20.2% carbon increase when removed, and the selected Pareto solution was invariant to the risk-aversion parameter λ over [0, 1], indicating that risk adjustment functions as a conservatism margin on the reported carbon target rather than a decision-altering preference. The framework advances construction DTs from descriptive monitoring tools toward risk-informed decision support, offering a transparent and reproducible benchmark that advances data-driven sustainability in construction scheduling and embodied carbon management. Full article

Proximity-based planning provides a normative framework for organising everyday services, but local accessibility does not necessarily correspond to the spatial extent of observed destination use. Building on the distinction between normative proximity and grid-level aggregated revealed activity space, this study develops an LBS-based diagnostic framework for planned–used life-circle mismatch. Anonymised non-home, non-work home-grid–destination-grid links observed at least four times in aggregate during the month were analysed for 995 residential grids of 500 m × 500 m in Lanzhou, China. The framework evaluates five dimensions: boundary coverage, activity-space scale, centroid displacement, directional morphology, and destination environments, and translates them into four planning-diagnostic types. The results reveal widespread divergence between planned proximity and revealed use. On average, 82.0% of aggregated recurrent visit weight lies outside the corresponding 15 min walking boundary, while the median distance between residential and activity centroids is approximately 4.18 km. The outside-dominant pattern remains evident when the analysis is restricted to destinations located in selected basic-service-dominant POI environments. Greater local POI supply is generally associated with lower outside-boundary reliance, whereas bridge distance is more clearly associated with centroid displacement than with outside-boundary share. The explanatory models have modest fit and identify grid-level associations rather than causal behavioural mechanisms. These findings support treating the 15 min boundary as a normative benchmark for local service provision rather than a presumed container of residents’ daily activities. Planning evaluation should therefore combine accessibility standards with behavioural evidence and differentiated diagnosis. The LBS data do not identify trip purpose, travel mode, socioeconomic characteristics, service quality, or individual necessity, and the results should be interpreted as grid-level diagnostic associations. Full article

Indoor wall surface flatness is a mandatory acceptance item in building decoration work, but it is still commonly checked using a 2 m straightedge and a feeler gauge. This manual method samples only a limited number of locations, depends on inspector judgement, and provides no full-surface spatial record. This study proposes a terrestrial laser scanning (TLS)-based automated workflow for wall surface flatness inspection that remains consistent with the Chinese national standard GB 50210-2018. After point-cloud registration and wall segmentation, a least-squares best-fit plane is established for each wall, and the signed point-to-plane deviation field is computed. A virtual 2 m straightedge operator is then applied at multiple positions and orientations to reproduce the code-defined measurement in which the maximum gap under the straightedge is taken as the flatness reading. The method was validated against paired manual measurements on a reference wall, showing a mean difference of 0.00 mm, a standard deviation of 0.20 mm, an RMSE of 0.20 mm, and a correlation coefficient of 0.98, with identical accept/reject decisions at the 4 mm limit. A residential case study involving four inspection zones and 42 measurement points further demonstrated that the workflow can generate code-comparable flatness readings, full-surface deviation maps, and localized rework guidance. The overall pass rate was 92.9%, with all non-conforming points concentrated in the living room, and the on-site inspection time was approximately halved compared to manual checking. The results indicate that the proposed TLS-based virtual-straightedge method provides a practical, traceable, and standard-compliant alternative for automated wall surface flatness acceptance. Full article

The aim of this study is to determine the reliability level (the probability of failure-free operation) of a masonry building with load-bearing walls based on the conducted experimental investigations. The objective of the study is to compare the obtained reliability and failure risk values with the corresponding values calculated using the results of the structural certification. In 2017–2018, and subsequently in 2023–2024, a comprehensive structural certification of the multi-apartment residential building stock was carried out for the first time in the city of Almaty. A total of 1609 multi-story masonry buildings with heights of two to four stories were identified. Based on the certification results, quantitative estimates of the prior and posterior probabilities of failure and reliability for masonry buildings were obtained for the first time. The recurrence of earthquakes was taken into account. The novelty of the study lies in the experimental investigation of a three-story masonry building of series 308. The dynamic excitation was generated by an inertial vibration machine installed on the floor slab. As the inertial load increased, the resonant vibration period changed by a factor of three. This indicates that the building underwent significantly nonlinear deformation. The structure sustained substantial damage. Using statistical simulation methods based on the experimental data, the prior probabilities of failure for masonry buildings were calculated. In this case, the seismic action was modeled as a non-stationary random process with the deterministic envelope proposed by F. F. Aptikaev. Probabilistic estimates of the reliability of masonry buildings were obtained from the certification results both with and without taking into account the recurrence of earthquakes. The obtained estimates of reliability and failure probability can be used to develop practical recommendations aimed at reducing risk and expected losses in the event of possible earthquakes. It is recommended that masonry buildings with load-bearing brick walls either be structurally strengthened or be demolished. Full article

Low-rise terraced housing dominates residential construction in non-metropolitan Taiwan, yet empirical evidence on priority divergence between developers and homebuyers in such markets remains scarce. Using a case study of Changhua County, we applied the Analytic Hierarchy Process (AHP) to quantify priority structures of 35 construction managers and 58 homebuyers. Both groups evaluated an identical five-dimensional hierarchy encompassing fourteen directly surveyed factors across location selection, housing price, financing, construction risk, and building planning. Consistency ratios were 0.028 (developers) and 0.019 (homebuyers). The results indicate substantial differences in relative priorities. Developers prioritized Construction Risk (0.368), with Government Regulations (A42) ranking first globally (0.152). Homebuyers prioritized Location Selection (0.412), with Transportation Convenience (A11) ranking first (0.223). The Location Selection dimension gap (0.221 points) and Construction Risk gap (0.251 points) represent the largest divergences. Factor-level rank inversions are pronounced: Transportation Convenience ranks first for homebuyers but fifth for developers; Government Regulations ranks first for developers but fourteenth for homebuyers. The findings suggest that supply-side and demand-side stakeholders may place different emphasis on project feasibility and residential use, with implications for site selection, floor plan design, and buyer communication in regional markets. Full article

The rapid surge of Electric Two-Wheelers (E2Ws) in high-density urban villages imposes severe strain on low-voltage residential distribution networks. Unlike formal Electric Vehicles, E2W charging is decentralized and highly constrained by short pedestrian walking thresholds, frequently forcing users to adopt non-compliant “fly-wire charging” when public facilities are scarce. Traditional top-down load models fail to capture these localized, micro-behavioral single-phase grid impacts. To address this deficit, this study proposes a GIS-integrated “Digital Sandbox” simulation framework that projects individual behavioral mutations directly onto feeder networks via a hyper-granular “particle tracking” mechanism, treating each E2W as an autonomous agent. As a case study focused on a representative urban-village area, we validate the model using field data from the site. A 30-day simulation reveals that unmanaged fly-wire charging generates a peak load of 17.64 kW (nearly double the public station peak) and accounts for 38.9% of aggregate energy consumption—concentrated within the top 10 buildings and coinciding with evening peaks, inducing severe phase imbalance. While the numerical results are case-specific, the framework itself is transferable to other service areas through re-calibration against local data. This foundational digital twin blueprint shifts E2W planning from guesswork to particle-level risk prediction. Full article

The practical performance of building-integrated solar systems in cold climates is strongly governed by temperature-grade matching between solar energy output and space-heating demand. However, year-round field evidence comparing solar thermal collectors, photovoltaic systems, and photovoltaic/thermal systems under the same building, climatic, and heating-network boundary conditions remains limited. This study conducted a year-round field evaluation of solar collector (SC), photovoltaic (PV), and photovoltaic/thermal (PVT) systems installed in an office building in Tianjin, China. Continuous operating data collected from November 2022 to October 2023 were used to assess seasonal thermal output, electricity generation, effective heat supply, solar utilization efficiency, carbon reduction, and payback period. During the heating season, SC exhibited the strongest direct-heating capability among the investigated systems, delivering 817.50 MJ/m² of useful heat. In contrast, under the investigated system configuration without heat-pump assistance, the outlet temperature of the PVT subsystem remained below the 45 °C direct-heating threshold, and its thermal output could not be directly utilized for winter space heating. This result is specific to the investigated operating conditions and does not exclude the potential application of PVT systems coupled with heat pumps or low-temperature heating terminals. During the non-heating season, the investigated PVT subsystem simultaneously produced electricity and usable low-temperature heat, with heat and electricity accounting for 61.3% and 38.7% of its useful output, respectively, indicating its potential for combined energy harvesting. Under the investigated climatic, system, cost, and energy-demand conditions, the entropy-weighted TOPSIS assessment ranked SC highest when non-heating-season heat demand was present, whereas PV was more suitable when such heat demand was absent. Furthermore, a demand–output matching method was developed to support SC/PV area allocation for different building types. Under the investigated climatic and energy-demand assumptions, the recommended PV area ratios were 54.5%, 67.4%, and 79.7% for residential, office, and commercial buildings, respectively. These results provide field evidence for effective heat evaluation, temperature-grade matching, and component selection in solar-assisted heating systems for cold-climate buildings. Full article

Announcements of major transport infrastructure can reorganize land markets long before construction begins, as expectations are capitalized into prices and building decisions—with direct implications for sustainable territorial planning. This study examines the real estate response to the 2024 announcement of the Torquemada airport project in the Valparaíso Region, Chile. We assemble a high-resolution microterritorial panel at the block–semester–land-use level, integrating three Chilean administrative registers: the SII cadastre (over 100 million construction lines across 16 semestral snapshots, 2018–2025), the F2890 conveyance records (1.49 million geolocated transactions), and the daily Unidad de Fomento series. We estimate a multi-outcome spatial difference-in-differences design, complemented by an event study, land-use heterogeneity analysis, local indicators of spatial association, placebo tests, spatial-weight sensitivity analysis, and the heterogeneity-robust Callaway–Sant’Anna estimator. We find a robust increase in new-parcel construction in the zone of influence—identified by an annual event study against never-treated controls whose pre-announcement coefficients are small and trendless, in sharp contrast to the uniformly positive pre-trends of the expansion and aggregate-stock series—together with selective appreciation of non-residential uses and no detectable effect on housing value. The expansion and aggregate-stock components are not separately identified: their pre-announcement trends are strongly non-parallel, so the corresponding fixed-effects coefficients are read as design-conditional associations. The evidence supports an activation of the extensive margin (new-parcel building) rather than a recomposition away from densification. We read the evidence as the anticipatory footprint of the announcement rather than a point causal effect. Detecting this footprint before construction enables anticipatory value capture and sprawl-containment policy while the planning window remains open. Full article

As the building sector shifts toward community-led energy efficiency (EE) initiatives to mitigate the climate crisis, scaling adoption requires understanding behavioral drivers. This study examines key drivers of EE measure adoption: perceived non-energy benefits, trusted advisors, and behavioral signaling. This research contributes to the energy policy and social science literature by empirically linking social trust with signaling preferences. It extends existing EE adoption decision theories by identifying distinct clusters of “expert” versus “close” advisors across demographic groups. Data from an experimental survey (n = 238) in Portland, Oregon, were analyzed using principal component analysis (PCA) and multivariate regression modeling. Results indicate that saving money and better indoor air quality are the most valued benefits with significant missing survey responses for other non-energy benefits, perhaps indicating a lack of respondent understanding. While family and contractors remain the most trusted advisors, findings highlight a clear split between expert actors and close social networks. Signaling via yard signs is the most preferred method for signaling EE behavior (31%), though “super-participators” prefer multi-channel signaling. These findings suggest that practitioners should thoughtfully leverage social networks and diverse signaling media to improve the salience and increase the adoption of residential energy efficiency programs. Full article

Restorative environment research has overwhelmingly privileged naturalness and greenness, neglecting morphological determinants and situated socio-cultural context. To redress this lacuna, we implemented a 3 (building layout) × 3 (visual openness) full-factorial field experiment across three residential sites in Urumqi, China. Employing a between-subjects protocol, Han and Uyghur residents completed 10 min free-viewing exposures, with perceived restorativeness (PRS-8), affect (PANAS-10), and physiological markers (baseline-normalized change rates) assessed. Building layout significantly modulated short-term cardiac autonomic recovery (heart rate, heart rate variability). Ethnic background, operationalized as a situated socio-cultural grouping variable, independently predicted heart rate variability (SDNN) and perceived restorativeness. Visual openness exhibited null psychological main effects yet significantly predicted peripheral physiological arousal (skin conductance level, skin temperature). Notably, positive affect manifested a significant three-way interaction (layout × openness × ethnicity), evidencing that visual openness acquires affective salience only under specific morphological and socio-cultural configurations, despite its non-significant psychological main effect. These findings indicate that residential morphology furnishes the macro-scale spatial context for immediate restoration, whereas visual openness operates as a micro-scale perceptual cue whose affective and restorative meaning is contingent upon morphological context and socio-cultural background. This study furnishes field-based evidence advocating the integration of macro-scale layout, micro-scale openness, and situated socio-cultural context in restorative residential landscape design. Full article

Rooftop rainwater harvesting (RWH) offers a practical adaptation option for Mediterranean cities where water scarcity is amplified by seasonal rainfall and climate variability. This study reports early findings from a simplified monthly water balance screening model for a typical residential building, driven by ERA5-Land monthly precipitation for Antalya and İzmir (Türkiye). Scenarios cover roof areas of 250–3000 m² and practical tank capacities of 2–100 m³ under a fixed non-potable demand of 0.20 m³/day. The model tracks monthly storage dynamics and supply demand in order to compute demand coverage and monthly reliability (i.e., fraction of months in which full demand is met). Reliability-based storage thresholds (≥0.80) are derived for four evaluation windows (1996–2010, 2011–2025, 1996–2025, 1950–2025) to explore climate sensitivity. In parallel, a guideline-style sizing which is consistent with the Turkish rainwater harvesting guideline is implemented using a three-day storage rule based on the wettest month potential. To enable a like-for-like comparison, the collection losses are harmonized by setting loss to 0.10 in the simulation and efficiency to 0.90 in the guideline method. The results show stable thresholds for Antalya but stronger period sensitivity in İzmir. They also quantify cases where guideline sizing does not achieve the target reliability under dry season constraints. This approach supports the rapid, climate-aware pre-design of small- to medium-scale urban RWH systems. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article