Search Results (736)

Reducing embodied greenhouse gas emissions in the initial design phase is essential for attaining low-carbon buildings, as the highest potential for reduction exists prior to the finalization of construction decisions. While Building Information Modelling (BIM) and Life Cycle Assessment (LCA) have been progressively integrated for embodied carbon evaluation, current frameworks are predominantly deterministic, offer minimal uncertainty quantification, and seldom utilize machine-learning-assisted optimization to facilitate design decision-making. This paper presents an uncertainty-aware BIM–LCA methodology to solve these shortcomings, integrating automated quantity takeoff, probabilistic carbon assessment, and explainable machine-learning optimization. The proposed methodology integrates IFC-based BIM models, Bills of Quantities (BoQs), and regional life cycle inventory databases to conduct a cradle-to-grave embodied carbon assessment. Quantities produced from BIM were checked against BoQ data, and the uncertainty related to material quantities and emission factors was assessed by Monte Carlo simulation. A machine-learning surrogate model was created with 1200 design samples to facilitate swift optimization, and SHapley Additive exPlanations (SHAPs) were utilized to determine the most significant design factors. A mid-rise residential structure in Chongqing, China, encompassing a gross floor area of 9750.03 m², was used as a case study. The baseline Global Warming Potential (GWP) was calculated as 514.29 ± 30.09 kgCO₂e/m² (A1–A5), with product-stage emissions (A1–A3) accounting for roughly 89.28% of total embodied carbon, predominantly from concrete and steel. Enhanced BIM maturity lowered uncertainty by roughly 20%. Optimization resulted in a 38.13% decrease in embodied carbon, reducing GWP to 318.21 kgCO₂e/m². SHAP research identified the percentage of material reuse and concrete composition as the primary factors influencing carbon reduction. The suggested framework offers a clear and replicable decision-support mechanism for low-carbon building design that accounts for uncertainty. Full article

This paper examines the vulnerability of wood-framed residential building envelopes to mold growth under projected future climate conditions, specifically elevated temperatures and humidity, a topic that is rarely addressed in the climate resilience literature. Residential exterior walls designed with interior insulation and vapor-retarding membranes may prove inadequate in future conditions. This study combined hygrothermal simulation and mold growth computation using morphed future weather data to analyze archetypal walls from two construction eras (1990s–2000s and present) in three US cities: New York City, Philadelphia, and Washington, DC. The results show substantial increases in mold’s prevalence under future climate scenarios for historical code-compliant walls, with even greater risk in walls with highly impermeable vapor barriers. Contemporary code-compliant walls showed no increased risk of mold. This framework for assessing future hygrothermal risks in building envelopes suggests a potentially widespread resilience problem. It underscores the need for further research, especially given the vast inventory of existing wood-frame buildings from earlier eras. Full article

Green retrofit of existing residential communities (GRERC) is critical for upgrading aging building stocks, but their true value extends far beyond physical improvements. A successful retrofit must simultaneously deliver economic, social, and ecological benefits. However, in practice, the value realization is often constrained by a complex network of interdependent factors. This study maps these underlying structures. We first extracted a preliminary set of variables from existing literature and case studies, validating them through survey data. By applying Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) techniques, subsequently, the reciprocal influences and the multi-level structural organization within the identified system were mapped. Our analysis isolates three foundational drivers: government evaluation standards, policy incentives, and ecological awareness. Crucially, these elements do more than exert direct pressure—they dictate the systemic transmission pathways that enable value realization. To bridge the gap between policy and practice, regulators must prioritize making evaluation standards practically actionable. Furthermore, scaling GRERC effectively will require redesigning incentive mechanisms to attract broader social participation and dramatically improving public access to retrofit information. 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

Meeting European carbon neutrality and energy performance targets requires large-scale rehabilitation of historic and traditional buildings, one of the construction sector’s key challenges by 2050. This will significantly increase demand for new materials and the production of waste, which already accounts for 39% of waste in Wallonia. From a circular economy and urban mining perspective, however, this waste can be viewed as a valuable resource for reuse and recovery. Despite this potential, Wallonia lacks detailed information on the material composition of its historic building stock, including material types, quantities, and reuse potential. Such knowledge is crucial for designing effective renovation strategies and promoting circular construction practices. The URMIBALI project addresses this gap by investigating traditional residential buildings built before 1919 in Liège (Belgium). Based on six case studies, the project develops two complementary research parts. The first part focuses on inventorying existing material stocks, estimating waste flows resulting from energy renovations, and evaluating the reuse potential of the main waste fractions. The second part proposes an initial digital methodology for the rapid and efficient acquisition of façade material data. The project’s novelty lies in its multi-material, bottom-up, and transdisciplinary approach, as well as in the creation of previously unavailable data on building-stock composition and the development of simple and flexible digital methods to acquire those data. These outputs improve knowledge of traditional buildings, support projections of renovation waste up to 2050, and facilitate urban-scale management of material flows, including transport, supply chains, and environmental impacts. This contribution presents the research methodology, key findings, and the transferability of the digital method to other building typologies and European contexts. Full article

Change orders in construction projects frequently lead to disputes, schedule delays, and cost overruns, particularly in the rapidly expanding construction sector of Saudi Arabia. Traditional techniques for resolving claims like litigation and arbitration are predominantly reactive in nature and do not facilitate proactive assessments of the impact of change orders before disputes materialize. A BIM-based framework is developed in this study to assess change orders in terms of time and cost with visualization functions through an integration of Autodesk Revit, Primavera and Navisworks, combined in a 5D virtual environment. The framework utilizes 3D modelling, scheduling and cost management (3D/4D/5D) supported by virtual reality (VR) visualization to create an interactive decision support platform for the project stakeholders. A real residential building case study was utilized to validate the framework, and a design modification was developed and analyzed using the BIM environment. The project cost has increased as a result of change order from SAR 411,437.26 to SAR 428,280.16, which is 4.1% of the total project cost. Also, there are deviations in the schedule which occurred from Month 4. The study results show that the suggested integrated BIM-based framework assesses the impact of change order, physically and visually, on the project time and cost that was required by the industry. The study is innovative in bringing together BIM layout, scheduling, and cost management with VR-supported visualization in a single decision support environment, allowing stakeholders to take into account the implications of change orders before they become real claims and disputes. The proposed framework allows transparent communication, collective decision making with stakeholders and early impact assessment, in contrast to existing approaches whose focus is primarily on claim resolution or improving coordination. This will improve project performance and how change orders are managed. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

With the large-scale integration of renewable energy and the deepening of electricity market reform, uncertainty in power system operation has increased significantly. This creates new challenges for multiple generation companies when they work together to develop generation schedules that balance economic efficiency and low-carbon goals. Most existing studies assume fixed loads and ignore the active regulation capability of the demand side under price signals and incentive signals. To address this gap, this paper proposes a low-carbon generation schedule optimization method for multiple generation companies. The method considers heterogeneous flexible loads. First, the paper decomposes flexible load adjustability into two components: price elasticity-based load shifting and incentive-based adjustable capacity. Using the price elasticity matrix method, the market clearing price serves as a known input. The load shifting amount under price elasticity regulation is pre-calculated for each park and treated as an exogenous parameter in the generation schedule model. This allows generation companies to directly use demand-side flexibility information during the planning stage. Second, the paper uses the proportion of residential and industrial loads as a core parameter. It characterizes the heterogeneity of four parks along two dimensions: elasticity coefficients and upper limits of adjustable capacity. Parks with a higher proportion of industrial loads have stronger flexible regulation capability. This result is consistent with real physical characteristics. It also provides a quantitative basis for generation companies to utilize flexible resources differently across parks and optimize their output arrangements. Finally, the paper uses the upward and downward adjustable capacity of each park as decision variables. It builds a multi-generator low-carbon generation schedule optimization model with heterogeneous flexible loads. Generator output constraints, power balance constraints, flexible load adjustable capacity constraints, and carbon quota constraints are all integrated into a single-level mixed-integer linear programming framework. This framework can be solved efficiently using commercial solvers. It helps generation companies develop optimal generation schedules that balance economic efficiency and low-carbon targets. Case study results show that combining price elasticity regulation with incentive-based adjustable capacity can effectively improve both the economic performance and low-carbon performance of generation schedules. Full article

Buildings account for nearly 30% of global energy-related carbon emissions. In rapidly developing economies, the operational phase of buildings represents a major and growing source of emissions. However, emission pathways in hot-summer-cold-winter (HSCW) regions remain understudied. This study analyzes carbon emission peaks and influencing factors in the operational phase of existing civilian buildings in Anhui Province. It integrates energy balance tables, the LEAP model, carbon emission factors, and the STIRPAT model. The energy balance table method disaggregates building energy consumption into urban, rural residential and public sectors. It adjusts for transportation energy by deducting specific proportions of gasoline and diesel from industrial, commercial, and residential sectors. Heating energy calculations are simplified because the region has a HSCW climate with limited centralized heating. The LEAP model projects emissions under four scenarios from 2020 to 2060. The STIRPAT model with ridge regression reveals that the permanent population and energy structure negatively influence residential emissions with elasticities of −2.646 and −1.465, respectively. This finding is consistent with the province’s energy transition, where coal use dropped from 28.48% in 2005 to 0.45% in 2020 and electricity use rose from 39.86% to 59.01%. In contrast, per capita GDP, building area, and energy intensity show positive effects. For public buildings, tertiary industry added value and energy structure are key determinants. Scenario analysis identifies the blueprint scenario as optimal, with residential emissions peaking at 34.29 million tons in 2025 and declining to 9.19 million tons by 2060 through measures such as 10% building retrofits by 2025, 75% energy-saving standards for new constructions, 50% retrofits by 2060, and renewable energy integration with building electrification, outperforming the baseline scenario that peaks in 2036 at 49.46 million tons and other intermediate scenarios. The study underscores that energy structure optimization significantly decouples energy consumption from emissions, offering actionable pathways for dual carbon goals through policy synergies in building efficiency, population management, and clean energy adoption to foster sustainable development and the construction industry’s low-carbon transition. Full article

Residential energy consumption exhibits substantial behavioral uncertainty and temporal heterogeneity, which pose challenges for demand-side management and residential load profiling. However, existing studies often focus on isolated temporal or spatial scales and predominantly employ hard clustering methods based on geometric distance metrics. To address these limitations, this study proposes a multi-scale residential load profiling framework utilizing the Gaussian Mixture Model (GMM) and nearly three years of hourly electricity consumption data from 13 residential buildings in Vancouver. First, schedule-driven and seasonal variations in residential energy consumption were examined through multi-temporal comparative analyses and paired-sample t-tests. The results indicate statistically significant differences between working-time and non-working-time energy consumption patterns in most buildings (p < 0.001). Second, individual-building clustering was performed to identify long-term intra-building daily load evolution characteristics, revealing 2–5 typical daily profiles across different households. Finally, inter-building clustering identified three representative residential groups characterized by low-energy stable patterns, high-energy intensive patterns, and intermediate commuting-oriented patterns. The average daily energy consumption levels of the three clusters were 13.11 kWh, 36.74 kWh, and 21.61 kWh, respectively. The proposed framework provides a data-driven approach for understanding residential energy-use heterogeneity across multiple scales and offers potential guidance for residential demand-side management and urban low-carbon energy planning. Full article

Seismic codes in high-risk earthquake zones magnify the embodied environmental impact of buildings by increasing structural mass. While the existing literature evaluates this burden holistically, this study isolates the environmental penalty of seismic design at the component level using building information modeling (BIM). Within this scope, an eight-story reinforced concrete residential building was modeled at LOD 300 and comparatively analyzed under TBDY-2018 (seismic) and a strictly theoretical TS-500 (gravity-only) baseline scenario. This gravity-only model acts solely as a mathematical isolation tool rather than a buildable design option. Using the CML 2001 methodology and Türkiye-specific environmental product declarations (EPDs), calculations covered the production (A1–A3), end-of-life (C1–C4), and recovery (Module D) stages of the building. Findings reveal that seismic mass increases create a nonlinear, asymmetric effect on environmental indicators. Increased concrete volume dictates the global warming potential (GWP), whereas steel reinforcement—driven by ductility demands—elevates the photochemical ozone creation potential (POCP) and acidification potential (AP) much more aggressively than concrete. Conversely, while seismic reinforcement provides a negative emission credit during the recovery stage (Module D), quantitative analysis reveals that this circular benefit is marginally small (offsetting approximately 2% of the steel-related GWP), proving mathematically insufficient to neutralize the massive upfront ecological debt. Consequently, the additional environmental penalty necessitated by seismic safety must be managed through early-stage BIM optimization and alternative mitigation strategies, such as seismic isolation. Full article

Building Information Modelling (BIM) workflows for prefabricated construction lack mechanisms that generate and compare alternative component configurations directly from a design model. Existing approaches define the optimisation search space manually and outside the model, address only one or two criteria, and treat the Work Breakdown Structure (WBS) as a post-design planning tool. This paper reinterprets the WBS as a generative decomposition mechanism. A WBS Engine decomposes the geometry of an existing BIM model into prefabricated subsystems before design decisions are fixed, producing the search space for optimisation without manual parametrisation. A Scenario Evaluator queries a database of 47 prefabricated components, and NSGA-II evaluates 60 configurations against four objectives. These are total cost, embodied carbon, assembly factor and number of lorry trips. Applied to a residential case study implemented in Dynamo, the prototype identified 16 non-dominated solutions. The best compromise configuration achieved a total cost of £150,444.01, 127,731.00 kgCO₂e, an assembly factor of 0.190 and 10 lorry trips. Wall module size accounted for 17.4% of cost variation, while floor module size governed assembly complexity. The findings show that BIM-WBS integration with multi-objective optimisation supports informed early-stage decisions in industrialised construction. Full article

The escalating energy consumption in China’s rural residences necessitates the adoption of targeted energy-efficient design strategies. However, existing studies have mainly focused on urban buildings or cold-climate rural residences, and insufficient attention has been given to form-based energy optimization for rural housing in hot summer and cold winter regions. Hangzhou was selected because it is a representative city in this climate zone, where rural residences face both summer cooling and winter heating demands. This study systematically investigates passive design pathways for rural residential buildings by optimizing architectural forms. We conducted in-depth field surveys and data analysis on 76 diverse samples, including both self-built and unified construction types, to establish three representative typical residential models (rectangular, L-shaped, U-shaped) for the Hangzhou region. DesignBuilder was employed to simulate the impacts of eight morphological elements—Shape Coefficient, building area, aspect ratio, orientation, number of floors, floor height, floor height ratio, and roof slope—on building energy consumption. The Gradient Boosting Decision Tree (GBDT) method was then used to quantify the nonlinear effects and relative importance of these elements. The results indicate clear nonlinear relationships between elements and the energy-saving rate. Floor height is identified as the most critical factor affecting energy consumption, followed by roof slope, with building area and other elements also showing significant influence. Based on the quantitative analysis, this study proposes energy-efficient design optimization strategies for rural housing in Hangzhou, offering a validated methodological framework and practical design references for the sustainable development of rural residences in hot summer and cold winter regions. Full article

Wind turbine blade (WTB) end-of-life waste is projected to increase significantly, yet no sustainable recycling solution with a clear energy, economic, and environmental (3E) assessment exists. This paper presents a validated 3E model of a WTB thermal recycling pilot (1 t/day) to benchmark recycled glass fibre (rGF) against virgin glass fibre (vGF) and identifies the throughput at which rGF becomes competitive. This subsequently leads to a projection of 3E performance at 5000 t/y plant capacity, at which rGF achieves approximately 46% lower specific primary thermal energy, 92% of the CO₂ emissions of vGF, and a selling price of 80% of vGF for a financial break-even. Building on this baseline, a novel combined material, heat, and power system is proposed and simulated, integrating the WTB recycling pilot with a 20 kWₑₗ/130 kWₜₕ organic Rankine cycle to serve residential buildings. Results show that coupling the pilot with 3000 m² of apartments yields a near net-zero CO₂ and energy-cost residential complex, with overall CO₂ emissions falling below those of standalone residential buildings combined with vGF production when more than 25 apartments are integrated. Full article

Accurate building performance forecasting is critical for the design and renovation of energy-saving structures, but existing methods face four key challenges: heterogeneous data fusion (sensor streams, design parameters, and environmental sequences), non-stationary physical time series, model interpretability, and sample efficiency (e.g., limited commissioning data). To address these challenges, this paper proposes Fusion-Grounded Forecasting (FGF), which is a framework integrating a gated adaptive fusion layer, deterministic trend-season decomposition, an additive predictor with component decomposition, and Bayesian regularization. This framework is designed for next-hour forecasting broadcast to hourly resolution using hourly sensor data and monthly design parameters. The dataset covers 36 months (approximately 25,920 h). In addition to the combination of existing modules, the novelty lies in the integrated architecture, in which interpretable constraints can adjust the fusion layer in both directions, with decomposition prediction alignment supporting component attributes. The framework is verified on a proprietary 36-month dataset from institutional buildings using standard prediction metrics (MAE, RMSE, MAPE, and directional accuracy) and ablation studies for comparison against 10 baselines: SARIMAX, GPR, LSTM, XGBoost, N-HiTS, Informer, Autoformer, NAM, a physics-informed hybrid, and TFT. FGF achieves a 3.1% MAPE and 92.5% directional accuracy in hourly cooling load forecasting. Ablation confirmed the contribution of each module: removing gated fusion increased the MAPE to 6.8%. Compared with manual feature engineering, the speed of the framework is increased by 1680 times, and the cost is reduced by 99.6%. The explanatory index (counterfactual reliability: 0.95; Stability of functional importance: 0.11) is in compliance with audit requirements. These results indicate that FGF connects descriptive physics with quantitative prediction. However, this study is limited to a single institutional building; transferability to residential, commercial, or industrial buildings requires further verification. While waiting for this verification, FGF has demonstrated its potential as a transparent and efficient tool to build performance models. Full article