How robot body configuration shapes human intervention during approach remains underexplored. We conducted a within-participants study with 41 participants, measuring final stopping distance, subjective comfort, and exploratory eye-tracking responses across four humanoid arm configurations and two spatial scales. Full forward arm extension increased stopping distance by approximately 31-36 cm relative to arms-down. Spatial scale primarily affected comfort and pupil responses without a detectable stopping-distance shift. We introduce the Configuration-Aware Limit Model (CALM), which translates stopping-distance distributions into configuration-dependent population-coverage boundaries. Estimated boundaries at 80

The early design exerts a decisive influence on a building’s whole-life carbon (WLC) performance, and understanding how practitioners perceive and apply WLC reduction strategies in this stage is critical for enabling effective decarbonization in building design practice. This study investigates cross-cultural differences in WLC understanding, motivation, and practice among AEC professionals in China and the U.S. An online questionnaire survey was conducted among 94 practitioners (50 China, 44 U.S.), the results reveal distinct regional patterns. U.S. respondents exhibit higher familiarity with life-cycle assessment (LCA), with a stronger focus on embodied carbon, whereas Chinese respondents emphasize operational energy. LCA literacy among architects remains generally limited in both countries. While both groups acknowledge the critical role of early design, only 23.1% of respondents report always implementing WLC strategies, all from the U.S. Motivation structures also diverge: Chinese practitioners are primarily regulation-driven, whereas U.S. practitioners are guided by internalized environmental commitment and professional ethics. In both regions, however, lack of client demand is identified as the most significant barrier. Across both regions, key design parameters such as window-to-wall ratio, structural materials, envelope materials, and HVAC systems are perceived as highly influential but often determined later, limiting opportunities for carbon optimization. Open-ended responses reveal carbon-centric terminology in U.S. practice versus energy-efficiency-oriented framing in China, reflecting differences in LCA maturity. These findings highlight the need for earlier integration of technical decisions, multi-level carbon benchmarks, and enhanced professional education to cultivate carbon design literacy and embed WLC thinking within early-stage design workflows worldwide.

Healthcare professionals endure chronic stress, yet current hospital break space design predominantly focuses on visual perception, often overlooking the synergistic potential of multisensory environments. This study investigates the stress-restorative effects of combined visual, auditory, and olfactory stimuli. Virtual environments were specifically constructed based on spatial element preferences derived from a survey of 180 healthcare professionals. Utilizing Virtual Reality (VR), an orthogonal experiment was conducted with 72 participants exposed to varying sensory conditions. Restorative outcomes were comprehensively assessed using psychological scales, including the Sensory Pleasantness Scale (SPS), the Positive and Negative Affect Schedule (PANAS), and the Perceived Restorativeness Scale (PRS), as well as physiological indicators, including electrodermal activity (EDA), heart rate variability (HRV), blood pressure (BP), pulse rate (PR), and blood oxygen saturation (SpO2). The results demonstrate that natural sensory combinations consistently yielded the most effective stress relief. Crucially, variance analysis indicated that the sensory modalities exert robust, independent, and additive restorative effects. This cumulative synergy demonstrates that maximum restoration is achieved through an integrated multisensory approach. Furthermore, a distinct sensory hierarchy emerged: visual stimuli primarily established the physiological baseline for recovery, whereas auditory and olfactory cues overwhelmingly dominated subjective psychological restoration. The findings advocate for a paradigm shift in hospital design from mere “visible” considerations to a holistic “hearable and smellable” multisensory ecosystem, providing evidence-based strategies to mitigate occupational burnout.

Exposure to nature influences urban dwellers’ well-being and happiness, thereby impacting urban sustainability. However, urban dwellers are exposed to nature in different ways: indirect exposure through window views, incidental exposure when walking along streets, and intentional exposure when visiting parks. Moreover, objective exposure does not necessarily align with how people perceive their exposure to nature. This study examines how three types of objective nature exposure—indirect, incidental, and intentional—provided by greenery and water bodies, along with perceived exposure, impact happiness in Tokyo, Japan. To measure the objective exposure, we use 3D photorealistic city information models, street view imagery, road network datasets, and remote sensing imagery. To measure happiness and perceived exposure, we use data from a national survey, focusing on the results from 10,798 residents in 801 neighborhoods in Tokyo. We first examine the associations between objective exposure and happiness using linear regression and non-linear machine learning models, and then the mediation of perceived exposure through Structural Equation Modeling. Results showed that perceived nature exposure has higher explanatory power for happiness than objective exposure. Views of greenery from windows (indirect exposure) and accessibility to parks (intentional exposure) influenced perceived nature exposure and happiness the most. The quantitative evidence suggests that urban planning align with human behavior, e.g., by prioritizing the improvement of greenery views from windows and park accessibility in Tokyo, to facilitate urban dwellers’ universal nature access for urban sustainability.

The early design stage presents a critical window for implementing climate impact reduction (CIR) strategies in buildings, as early design decisions can determine 70-80% of a building's whole life climate impacts. However, current research predominantly examines single CIR strategies, offering little insight into their interactions in early design. We conducted through a systematic literature review (123 publications, 2015-2025) and cross-impact analysis to develop a comprehensive framework of 12 CIR strategies for early building design. The framework summarizes strategies based on whether they are architect-led, engineer-led, or encompass cross-disciplinary collaboration. The cross-impact analysis visualized the interactions between the studied strategies, identifying 38 strong positive, 47 moderate positive, and 7 moderate negative interactions, with no strong negative interactions observed. Overall, most strategies were found to be mutually reinforcing. Among them, cross-disciplinary strategies such as Implement Early-stage Building LCA (Cross1) and Promote Resource Circularity (Cross3) exhibit the highest synergistic potential, serving as key linkers that bridge disciplinary silos. The findings suggest that integrated design approaches that require multi-stakeholder collaboration are fundamental for effective climate impact reduction. The innovation of this study lies in offering a comprehensive synthesis of CIR strategies for the early design stage, while uncovering their potential synergies and trade-offs. The proposed framework and cross-impact analysis results offer AEC professionals practical guidance for implementing coordinated CIR strategies, thereby supporting the shift from current practices toward integrated, systems-based approaches to sustainable building design.

Climate change has become one of the most urgent global challenges, demanding rapid and deep decarbonization across all sectors. The building sector alone accounts for over 40% of total national carbon emissions, with a majority of lifecycle carbon performance effectively locked in during early-stage spatial decision-making. This makes pre-design programming a critical intervention window for achieving zero‑carbon urban development. However, the conventional design paradigm of “spatial form follows function” is inherently limited in addressing the complex coupling among climate conditions, spatial morphology, and carbon flows at the conceptual design stage. Grounded in architectural theory, this review traces the evolution of spatial decision-making from experience-driven traditions toward performance-oriented approaches, and critically examines the structural limitations of conventional paradigms in the low-carbon transition. Building on this analysis, an integrative conceptual framework —“ spatial form follows carbon flow”—is proposed to reposition carbon performance as an intrinsic driver of spatial design. Within this framework, artificial intelligence (AI) is positioned as an enabling technology whose core value lies in three aspects: (i) establishing quantitative relationships between spatial morphology and carbon performance, (ii) enabling real-time performance feedback during the design process, and (iii) supporting multi-objective optimization in spatial decision-making. Finally, this review discusses key challenges and future directions, including data availability, cross-scale coupling between building and urban systems, and the interpretability of AI-enabled decision-making. This work aims to provide an integrative conceptual foundation for advancing architectural design and programming theory in the low-carbon era.

How neighborhoods are defined has long been a key issue in urban research. Traditional approaches primarily rely on census data. However, such demarcations have been increasingly criticized for failing to capture the area that residents perceive as their neighborhoods. These ‘perceived neighborhoods’ more accurately align with their lived experiences. Therefore, identifying perceived neighborhoods would be helpful for understanding residents’ everyday activities and addressing their needs through planning and design. Recently, emerging evidence suggested that residents’ neighborhood perception is significantly influenced by urban form. However, existing studies often oversimplify the influence, tending to examine a single aspect of urban form independently but neglecting the interplay of different aspects, and produce partial understandings. Drawing on diverse urban datasets and cognitive mapping surveys from Singapore, this study develops a machine learning framework to bridge this gap. Using SHAP analysis, the results indicate that residents’ neighborhood perception is primarily affected by urban form features associated with their daily activities—such as footpath density and commercial facility distribution—rather than visual ones. Moreover, how residents perceive a place is affected not only by features of that place itself, but also by those of their own places of residence. It suggests that neighborhood perception is a relative process. Additionally, the findings uncover nonlinear effects of certain features, wherein the effects can diminish or even reverse when over-supplied. This study offers an in-depth understanding of the complexity of urban form’s influence on residents’ neighborhood perception and provides insights for responsive neighborhood planning and design.

Despite walkability has become a critical concern for citizens, academic researchers, and policymakers, the conventional walkability methods limited validity and applicability in different urban contexts. One of the key reasons is that the index ignores individual microscale walking preference behaviors that are important in assessing walkability. The incorporation of subjective decision preferences into walking decision-making process for service facilities represents a significant advancement for walkability measurement. To address this gap, this study introduces a novel walkability measurement approach based on discrete choice stated preference (DCSP) to investigate individual walking decision behavior preferences for service facilities, termed the Facility Stated Preference (FSP) method, and reveals the decision utility of walking preferences for 21 types of service facilities using three key variables: Use Frequency Preference (UFP), Use Diversity Preference (UDP), and Spatial Distance Preference (SDP). To validate the effectiveness and applicability of the FSP method, a case study is conducted across eight diverse communities in Qixinggang. The findings indicate significant spatial disparities in walkability scores across different geographical units, with modern residential areas along urban arterial roads exhibiting better walkability compare to older communities in the south and north. Additionally, the study highlights varying levels of walkability associated with different types of service facilities, with subway stations, bus stations, and primary and middle schools emerging as key contributors to overall walkability. These insights provide a deeper understanding of individual walking decision preferences and enhance predictive capabilities for service facility planning, serving as a valuable resource for urban planning authorities and policymakers in forecasting walking demand and optimizing service facility allocation.

Open-plan office shared spaces are essential to employee well-being, reflecting genuine behavioral responses to environmental quality. This study proposes a data-driven approach using multi-sensor spatiotemporal data fusion for automated spatial evaluation. In a representative open-plan technology office building, ubiquitous sensing collected approximately 210 thousand positioning records and 5.6 million light-thermal environment measurements, together with structured spatial design data, from 25 breakout areas over 21 days. The data revealed distinct spatial patterns of behavior and environment, allowing shared spaces to be classified into four stayduration types. Based on a 10-min average stay interval, univariate regression identified key factors influencing occupancy. Random forest and interpretable models further confirmed that openness, distance to workstations, temperature, illumination, and area were the most influential variables affecting space utilization. Subjective comfort assessments validated the reliability of the sensor-based results, showing a consistency coefficient of 0.81. These findings establish a multi-dimensional framework for behavioral research in the built environment and provide practical guidance for architects, facility managers, employees, and corporate administrators.

ObjectiveThe determination of indicator weights in multi-criteria group decision-making (MCGDM) represents a critical issue in architectural programming and post-occupancy evaluation (POE). Traditional approaches often model expert weights and indicator weights independently, which leads to potential inconsistencies and suboptimal decision outcomes. This study aims to develop an iterative group decision-making model that simultaneously optimizes expert weights and indicator weights, enhancing the accuracy, reliability, and practical applicability of weight determination in complex engineering contexts.MethodsAn iterative optimization model was proposed to integrate the determination of expert weights and indicator weights. First, an expert weight optimization model was established based on the consistency between individual expert evaluations and aggregated group evaluations. This model incorporated the relative importance of different indicators through a weighted distance metric derived from a specially defined A-norm, which generalized the standard Euclidean distance. Second, indicator weights were calculated by aggregating individual expert evaluations weighted by their corresponding credibility levels. Finally, an iterative framework was designed to enable mutual calibration between expert weights and indicator weights until convergence was achieved. The model was implemented algorithmically and validated using two real-world case studies, including green performance evaluation during architectural programming and building post-occupancy evaluation.Results and DiscussionsNumerical experiments demonstrated that the proposed iterative model converged reliably under various initial conditions. In the green performance evaluation case, which involved nine experts and 96 indicators, the model converged within eight iterations, with differences between successive iterations falling below 10‒10 in the infinity norm. The resulting expert weights exhibited significant variation, reflecting differences in expertise across building types, such as office buildings and tourism buildings. Similarly, in the POE case involving five experts and 69 indicators, the model converged within seven to nine iterations, and the resulting weights effectively captured functional differences between museum buildings and stadium buildings. Compared to baseline methods that assigned equal weights to experts, the proposed model generated more discriminative and contextually appropriate indicator weights. For example, in the office building case, the highest weighted indicators included room temperature, sunshade facilities, and noise control, whereas in the tourism building case, rest spaces and accessibility planning received higher weights, which were consistent with functional priorities. The model also demonstrated robustness to different initial conditions and maintained convergence regardless of the order of weight updates.ConclusionsThis study presents a novel iterative group decision-making model that simultaneously determines expert and indicator weights by leveraging their mutual influence. The proposed model improves upon existing approaches by incorporating indicator importance into the calibration of expert weights, achieving more consistent and context-aware weight assignments. The convergence and practical applicability of the model are empirically validated through real-world case studies in architectural programming and POE. The method provides a mathematically rigorous and engineering-oriented approach to MCGDM, with potential applicability in other domains that involve multi-expert and multi-criteria evaluation processes.

The open-plan office layout has gained popularity in modern interior design, yet its benefits for occupants remain unclear. This study uses psychological, physiological, and cognitive testing to assess how spatial dimensions impact work performance. In a CAVE laboratory with VR, three settings-small (9 x 9 m), medium (18x9 m), and large (27x9 m)-were simulated, with 52 participants completing the Stroop and Digit Span Tests. Data were collected via electroencephalography (EEG), eye tracking, skin conductance levels (SCL), and self-reported State-Trait Anxiety Inventory (STAI) scores. Results show that smaller spaces (9 x 9 m) improve cognitive performance, while larger spaces may benefit collaborative tasks. An economic analysis demonstrates that smaller spaces achieve a 23.37 % increase in benefits compared to larger spaces, sufficient to offset the higher construction costs. These findings emphasize the impact of spatial dimensions on employee well-being, health, and productivity, introducing a brain-computer interface framework for spatial evaluation to promote a healthier, more efficient workplace.

Climate change and rapid urbanization exacerbate urban environmental challenges, particularly the urban heat island effect (UHI) and air pollution. Urban street canyons are critical hotspots for both heat stress and traffic-related pollution. This thermo-pollutant coupling threatens public health, increases building energy consumption and carbon emissions, and hinders urban sustainability. Moving beyond single-pollutant approaches, this study proposed a nature-based solution integrating vertical greening with street trees. Utilizing a validated 3D street canyon multifield coupling model, we analyzed thermal and pollution distributions under three scenarios: no greenery, street trees only, and street trees combined with vertical greenery. Computational fluid dynamics (CFD) simulations quantified vegetation impacts on wind flow, temperature distribution, and particle deposition, elucidating the regulatory mechanisms. Results demonstrated the high efficacy of integrated vegetation in co-mitigating heat and pollution. Street trees alone reduced near-building temperatures by an average of 0.2 degrees C through shading and transpiration. Adding vertical greenery enhanced cooling by up to 0.6 degrees C and promoted more uniform temperature distribution. For air pollution control, the combined system achieved a remarkable 95 % particle removal rate, significantly outperforming street trees alone (71 %) and reducing vertical pollution stratification. This study provides critical insights for urban nature-based design and offers a vegetation configuration paradigm for high-density streets to maximize environmental co-benefits. By addressing the coupled thermo-pollutant challenge, it can help enhance understanding of greening's regulatory role in complex urban systems and support sustainable urban development.

As climate change intensifies, the building sector—responsible for around 40% of global energy consumption and 35% of carbon emissions—faces pressing demands for decarbonization. Green infrastructure, which integrates natural processes into spatial design, has emerged as a promising strategy to enhance buildings’ climate adaptability and reduce carbon footprints. Unlike conventional landscape elements, building-integrated green infrastructure (e.g., green roofs, vertical greening, urban green belts) performs multifunctional roles such as thermal regulation, stormwater management, shading, and ecological interaction. Its integration is evolving from superficial embellishment to embedded design logic, enabling synergies between architectural form, energy performance, and environmental benefits. However, systematic understanding of its emission reduction mechanisms, performance variation under different climatic and operational conditions, and cost-effectiveness remains limited. This paper provides a comprehensive review of the spatial integration models, thermal and carbon-reducing mechanisms, and quantitative evaluation methods. This review introduces a structural-integration-based classification of GI by embedding depth (roof, façade, site, material) and proposes a dual-path carbon reduction framework linking direct and indirect mechanisms. It also identifies key influencing factors and outlines future directions for transforming green infrastructure into actively regulated, cross-scale ecological systems. This work aims to bridge architectural design and environmental performance, offering theoretical support for the low-carbon transformation of the built environment.

Proximity to nature aids in stress recovery, but the impact of indoor spatial openness on this process is underresearched. An architectural model with three types of interior openness-closed, semi-open, and open-was designed, and a within-group experiment with 52 participants was conducted. Our data-centric approach utilized psychological measurements (State-Trait Anxiety Inventory test (STAI), post-experiment questionnaires) and physiological measurements (electroencephalogram (EEG), eye-tracking, skin conductance level (SCL)) in a Cave Automated Virtual Environment. A linear mixed-effect model assessed the dose-response relationship between openness conditions and ergonomic stress recovery outcomes. Larger open spaces significantly reduced stress, increased alpha/(3 and 0/(3 ratios, decreased pupil diameters, and lowered SCL. Significant correlations were found between STAI scores and frontal 0/(3 ratios (r = 0.25), post-experiment evaluations and pupil diameter (r = 0.20). These findings suggest that open environments may reduce stress through brain and visual stimulation. Additionally, alpha/(3 and 0/(3 ratios in the occipital and left frontal lobes, along with eye movement metrics, were sensitive to environmental changes. The integrated stress recovery value moderately correlated with visual fixation area coverage (r = 0.52) and spatial openness evaluations (r = 0.50). Our results support the conclusion that architects and designers can promote stress recovery by creating spacious layouts in coordination with other stress-reducing features.

Fine-grained spatial utilization enhances post-occupancy evaluation (POE) precision. Traditional methods are limited by lower spatiotemporal resolution and smaller datasets, whereas indoor positioning systems offer high-precision occupancy data. The proposed indoor space utilization index combing spatial scale, occupancy points, and duration of stay to evaluate the distribution of spatio-temporal behavior and utilization rates within functional zones. Among a two-month period, a dataset of over 200,000 unlabeled behavioral data was collected in an open-office building using Wi-Fi and Bluetooth positioning systems. Through data processing and point projection, it is found that: (1) Point data shows spatio-temporal variations across floors, weekdays versus weekends, and different times of day. (2) High-density, long-duration workstation areas are highly utilized, while low-density, short-duration public spaces are underutilized. (3) Multi-functional atriums, open discussion areas, and entrance-linked elevators are most utilized, reflecting employee work patterns. Analysis of Kullback–Leibler divergence across different spatio-temporal units confirmed the reliability of conclusions, demonstrating that 20 weekdays of valid mobile phone data yield consistent results irrespective of grid sizes. This paradigm leverages long-term, non-intrusive, high-precision positioning data from Wi-Fi and Bluetooth systems to accurately track space utilization dynamics in real time across various scales, supporting human-centered POE.

Traditional design decision-making is usually a process of adapting and reusing the paradigm at the architect’s disposal based on intuition and experience, which helps to provide acceptable solutions to known design problems quickly but makes it challenging to generate new spatial types beyond the existing paradigm. The emergence of informal learning behaviors has caused changes in public learning spaces, and traditional design decision-making processes have struggled to provide better solutions. Based on 620 questionnaires, this study combines statistical and space syntax analysis to obtain the relationship between user preferences and space. It uses multi-agent simulation to interpret preferences into spatial forms to assist design in obtaining a more suitable spatial layout pattern for informal learning scenarios. Proposing a design decision-making method to solve complex behavior-driven problems effectively, this research will help to integrate research and design effectively, improve the efficiency and accuracy of design decision-making.

The life-cycle assessment method, which originates from general products and services, has gradually come to be applied to investigations of the life-cycle carbon emissions (LCCE) of buildings. A literature review was conducted to clarify LCCE implications, calculations, and reductions in the context of buildings. A total of 826 global building carbon emission calculation cases were obtained from 161 studies based on the framework of the building life-cycle stage division stipulated by ISO 21930 and the basic principles of the emission factor (EF) approach. The carbon emission calculation methods and results are discussed herein, based on the modules of production, construction, use, end-of-life, and supplementary benefits. According to the hotspot distribution of a building’s carbon emissions, carbon reduction strategies are classified into six groups for technical content and benefits analysis, including reducing the activity data pertaining to building materials and energy, reducing the carbon EFs of the building materials and energy, and exploiting the advantages of supplementary benefits. The research gaps and challenges in current building LCCE studies are summarized in terms of research goals and ideas, calculation methods, basic parameters, and carbon reduction strategies; development suggestions are also proposed.

Building Information Modeling plays an important role in laboratory design. The reasonable layout of the outdoor equipment pipeline is the key to supporting the efficient operation of the laboratory, increasing the flexibility of the laboratory space module, and planning a holistic smart campus space. However, the traditional BIM model lacks convenient visualization and interoperability in the early stage of the program and may lead to inconsistency. This paper aims to propose an integrated visual optimization model toolkit of the equipment and piping using the Rhino + Grasshopper platform. Based on this digital-twin model, the horizontal and vertical space required for the outdoor equipment piping system can be quickly calculated in the site planning stage. The workflow improves the efficiency and accuracy of equipment pipeline system design and reduces multiple design changes. After verifying the validity of the model through two virtual scenarios, it was demonstrated in a real laboratory campus. In the construction drawing stage, the toolkit was used to check whether the interspace of different professional pipeline meets the requirements. This paper expands the design concept, emphasizes the coupling relationship between pipelines and building space, and integrates the experimental and building space concepts throughout the design process.

Rapid urbanization has increased the urban density and functional diversity, exacerbating environmental challenges such as urban heat islands (UHI), increased building energy consumption and carbon emissions, etc., hindering the sustainable urban development. Addressing these challenges requires innovative solutions that could offer the strategic cooling of urban buildings. Vertical greenery, affixed to building fa & ccedil;ades, offers a promising approach to provide benefits of cooling, energy savings, carbon reduction and urban space saving. However, there is a lack of quantitative design for integrating vertical greenery into complex urban buildings. Therefore, this study conducts a systematic investigation for low-rise, mid-rise, and high-rise buildings incorporating vertical greenery, while performing comprehensive assessments on indoor and outdoor thermal conditions, building energy usage, and carbon emissions. The findings suggest that low-rise buildings benefit from a vertical greenery layout, while mid-rise and high-rise buildings are better suited for a horizontal greenery layout. Compared to scenarios without greenery, buildings with vertical greenery experience a maximum reduction of 0.66 degrees C in outdoor air temperature and 0.72 degrees C in indoor air temperature, along with a 5.9 % decrease in energy consumption and carbon emissions. This study addresses urban challenges through a quantified vertical greenery design, offering valuable insights for sustainable urban development.

Spatial data acquisition technology is constantly evolved. However, there is a lack of collation of high-precision data applications and integration of subjective and objective spatial assessment data. The research question of this paper is how to improve the quality of built environment through the analysis of integrated spatial assessment data. A total of 987 related papers were searched and 121 articles were screened for deep review. Collated by Meta-analysis, the findings of literature review can be summarized as follows: 1) Time-space integration. In the spatial dimension, it evolves from two-dimensional to multi-dimensional. In the temporal dimension, the behaviour sequence of users in the field is analysed and upgraded from static to dynamic. 2) Object dimension expansion. By taking the individual as the unit of analysis to study the micro-scale perceptual characteristics and mechanisms to group trajectory movement analysis. 3) Multiple data information. The rapid development of personal smart devices, the Internet of Things and social media makes the types of spatial assessment data more abundant. 4) Multi-scale transition. Due to the improvement of spatial assessment data accuracy, the object of assessment gradually transitions from urban scale to near-human scale. Furthermore, this paper proposes a spatial assessment framework based on behavioural research and spatial assessment data fusion techniques.