CONSTRUCTING BIM-DRIVEN MULTI-HAZARD RESILIENCE INDICES COMBINING CLIMATE-STRESS SCENARIOS WITH SUSTAINABILITYWEIGHTED CONSTRUCTION DECISION OPTIMIZATION TECHNIQUES.

Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [163] CONSTRUCTING BIM-DRIVEN MULTI-HAZARD RESILIENCE INDICES COMBINING CLIMATE-STRESS SCENARIOS WITH SUSTAINABILITYWEIGHTED CONSTRUCTION DECISION OPTIMIZATION TECHNIQUES. Adeyemi Michael Adejumobi Purdue University, USA ABSTRACT The increasing frequency and severity of climate-induced hazards have intensified the need for construction systems capable of demonstrating high resilience while meeting stringent sustainability requirements. Traditional risk assessment frameworks often evaluate hazards in isolation and lack the computational depth needed to integrate sustainability-based decision metrics. To address this gap, this study develops a BIM-driven methodology for constructing multi-hazard resilience indices that incorporate climate-stress scenarios and sustainability-weighted optimization techniques. At the broader level, the approach links Building Information Modeling with advanced environmental analytics, enabling the extraction, simulation, and integration of thermodynamic, structural, and ecological performance attributes directly from digital project models. These attributes are evaluated against projected climate loads including heatwaves, extreme precipitation, and wind intensification to quantify system vulnerabilities across temporal and spatial scales. The methodology narrows in scope by formulating resilience indices that blend hazard-response behaviors with sustainability criteria such as embodied carbon, operational energy use, and long-term resource efficiency. A multi-objective optimization engine assigns dynamic weights to sustainability indicators based on scenario severity, economic constraints, and construction sequencing requirements. Through this integration, BIM functions not only as a geometric repository but also as a computational platform for adaptive decision-making. The resulting indices allow project teams to evaluate trade-offs between resilience-enhancing interventions and sustainability goals, ensuring that mitigation strategies do not inadvertently increase lifecycle impacts. The research provides a novel pathway for embedding resilience and sustainability into a unified digital framework, offering actionable insights for planners, engineers, and policymakers confronted with climate uncertainty. It also establishes the foundation for advanced predictive analytics and decision-support systems capable of guiding sustainable construction choices under multi-hazard conditions. Keywords: BIM-driven resilience, multi-hazard assessment, sustainability optimization, climate-stress scenarios, construction decision-making, resilience indices 1. INTRODUCTION 1.1 Background: Climate hazards, resilience gaps, and the sustainability imperative Climate-induced hazards such as extreme heat, intense rainfall, storm surges, and wind amplification continue to reshape global construction risk profiles, exposing infrastructure systems to unpredictable stressors that increasingly exceed traditional design envelopes [1]. These escalating threats reveal significant resilience gaps, particularly in how construction projects anticipate, absorb, and recover from multi-hazard disruptions. Conventional engineering models often treat hazards independently, overlooking compounding effects that arise when climate variables interact dynamically across spatial and temporal scales [2]. Sustainability considerations add an additional layer of complexity: projects must now meet stringent environmental expectations while simultaneously enhancing structural robustness, operational continuity, and lifecycle performance [3]. Integrating resilience with sustainability is therefore no longer optional but fundamental for achieving long-term infrastructure viability. This dual imperative underscores the necessity of computational tools capable of modeling hazard behaviors, assessing vulnerability pathways, and evaluating the environmental implications of protective interventions [4]. BIM emerges as a powerful catalyst for this transformation, offering a data-rich environment that centralizes geometric, material, operational, and environmental information. When properly leveraged, BIM can facilitate advanced simulations and decision analytics that bridge resilience theory with sustainability-driven design and construction practices [5]. Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [164] 1.2 Problem statement: Fragmented hazard assessment and lack of integrated sustainability weighting Despite notable advancements in modeling climate hazards, most assessment approaches remain fragmented, relying on isolated analyses that fail to capture interdependencies between structural exposure, operational performance, and sustainability outcomes [6]. Current resilience frameworks typically emphasize structural capacity or hazard intensity but rarely incorporate weighted evaluation of sustainability indicators such as embodied carbon, energy demand, water usage, or environmental degradation potential [7]. This omission limits the ability of project teams to select interventions that balance protective performance with ecological responsibility. Furthermore, hazard assessments are often disconnected from BIM environments, resulting in duplicated data, inconsistent assumptions, and reduced analytical transparency [8]. Without integrated sustainability weighting, decision-makers risk adopting resilience-enhancing measures that unintentionally increase lifecycle impacts or compromise long-term environmental goals [9]. Consequently, a unified methodology is needed one that blends multi-hazard simulation, resilience scoring, and sustainability-weighted optimization into a seamless digital workflow capable of guiding computationally informed construction decisions [10]. 1.3 Research aim, scope, and contributions This study aims to develop a BIM-driven computational framework for constructing multi-hazard resilience indices that incorporate climate-stress scenarios alongside sustainability-weighted decision optimization. The scope spans structural, environmental, and thermodynamic domains, enabling a holistic evaluation of how buildings respond to dynamic climate stressors and how sustainability metrics influence resilience-based choices [3]. The framework integrates hazard simulations, BIM-derived data, and multi-objective weighting models to quantify trade-offs among performance, risk, and ecological objectives [6]. Key contributions include: (1) a unified formulation for resilience indices that account for both hazard intensity and sustainability impacts; (2) a BIM-centered computational workflow linking geometric, material, and environmental datasets with advanced analytics; and (3) a decision-support architecture enabling scenario-adaptive optimization across competing priorities [8]. Collectively, these contributions provide a structured pathway for embedding resilience and sustainability into construction engineering practice, addressing gaps identified in contemporary multi-hazard assessment methodologies [2]. 2. LITERATURE REVIEW 2.1 Multi-hazard resilience concepts in construction engineering Multi-hazard resilience has become a central analytical focus in construction engineering as climate-related threats intensify and interact in ways that exceed traditional single-hazard design assumptions [7]. Resilience encompasses the capacity of a built asset to anticipate, withstand, adapt to, and recover from disruptive events, but contemporary climate scenarios introduce complex stressors such as cascading failures, concurrent hazards, and nonlinear damage progressions. Construction systems exposed to extreme winds may simultaneously experience thermal stress or precipitation-related degradation, requiring holistic analytical frameworks rather than isolated hazard models [8]. A resilient system must therefore account not only for structural robustness but also for functionality preservation, recoverability, and lifecycle adaptability. Recent research emphasizes integrating material behavior, structural dynamics, energy performance, and external hazard forces into cohesive assessment models capable of reflecting how hazards accumulate or compound over time [9]. In practice, this means moving beyond static capacity checks toward dynamic simulation environments that capture evolving vulnerability pathways. Multi-hazard resilience further involves quantifying performance degradation under different intensities, durations, and sequences of events, enabling decision-makers to prioritize interventions based on a broader understanding of risk interaction. As construction environments become more complex and sustainability pressures rise, multi-hazard resilience has emerged as a comprehensive performance indicator reflecting both immediate hazard preparedness and long-term system longevity [10]. 2.2 BIM as a digital backbone for hazard-informed decision-making Building Information Modeling (BIM) has evolved into a central integrative platform for hazard-informed decision-making due to its ability to consolidate geometric, material, operational, and environmental data within a unified digital environment [11]. Traditional hazard models often rely on fragmented datasets and disparate simulation tools, making consistency and traceability challenging. BIM addresses these limitations by offering a structured information ecosystem where hazard parameters, structural attributes, and sustainability indicators can be linked seamlessly [12]. This integration allows hazard simulations such as dynamic wind modeling, thermal Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [165] stress propagation, or water infiltration analysis to be anchored directly to asset-specific BIM objects rather than generic assumptions. As a result, BIM enables fine-grained vulnerability assessments sensitive to design variations, construction phasing, and material selections. Moreover, BIM facilitates scenario-based analysis by allowing users to embed climate projections, maintenance schedules, and operational constraints into the model, generating multidimensional insights into hazard exposure and performance degradation across the asset lifecycle [13]. Through interoperability with simulation engines, BIM can also produce parametric hazard models that react dynamically to updated environmental conditions or design changes. Importantly, BIM supports visualization capabilities that make hazard pathways and resilience deficits interpretable for both engineering specialists and project stakeholders [14]. As decision-making becomes increasingly dependent on cross-disciplinary inputs, BIM functions as the digital backbone that unifies structural engineering analytics, climate modeling, sustainability metrics, and economic evaluations. This consolidation enables construction teams to develop more coherent, evidence-based strategies for resilience enhancement. Ultimately, BIM’s ability to serve as a living, data-rich representation of the built environment positions it as an indispensable tool for developing multi-hazard resilience indices grounded in real asset properties rather than abstract assumptions [15]. 2.3 Sustainability-weighted optimization approaches: limitations in current practice Although sustainability is widely recognized as essential to modern construction practice, current optimization approaches often fail to integrate sustainability within hazard resilience frameworks in a meaningful or computationally consistent way [16]. Many sustainability evaluation tools operate independently of structural or hazard simulations, focusing instead on isolated metrics such as embodied carbon, waste reduction, or operational energy performance. While valuable, these tools rarely account for how sustainability interventions influence hazard vulnerability or, conversely, how resilience measures affect lifecycle environmental outcomes. For instance, strengthening a structure may improve resilience but increase embodied emissions, whereas material substitutions may lower carbon footprints while reducing hazard resistance. Existing optimization algorithms generally lack mechanisms to balance these competing priorities within a unified mathematical or computational space. Furthermore, sustainability weighting is typically static, failing to adjust to changing hazard intensities, evolving climate forecasts, or project-specific constraints. Without dynamic integration, sustainability assessments remain peripheral rather than central to resilience decision-making. This gap underscores the need for optimization approaches capable of assigning scenario-sensitive sustainability weights and embedding them directly into multi-hazard resilience indices, ensuring that decisions reflect holistic lifecycle impacts rather than partial evaluations [17]. 2.4 Research gap and motivation for integrated indices Despite progress in resilience engineering, BIM adoption, and sustainability modeling, a cohesive framework that integrates multi-hazard resilience with dynamic sustainability weighting remains absent. Existing methodologies excel at hazard prediction or sustainability evaluation individually, yet none systematically merge these dimensions within a unified computational workflow grounded in BIM data structures [12]. This lack of integration limits interpretability, undermines decision-making transparency, and increases the risk of selecting measures that improve resilience while worsening environmental performance or vice versa [8]. A more comprehensive model must therefore compute resilience and sustainability jointly, dynamically adjusting weights based on hazard severity, lifecycle considerations, and project priorities. Integrated indices offer the ability to quantify these relationships in a single numeric measure, bridging theoretical models with practical construction workflows. The present research is motivated by this precise gap: the need for a multidimensional, BIM-driven resilience index that captures hazard interactions and sustainability impacts simultaneously, producing actionable insights for construction engineering management [10]. Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [166] Figure 1: Conceptual landscape of multi-hazard resilience, BIM integration, and sustainability-weighted optimization. 3. METHODOLOGICAL FRAMEWORK 3.1 BIM-driven data extraction and semantic enrichment 3.1.1 Structural, thermodynamic, and environmental attribute modeling BIM-driven data extraction begins by capturing structural, thermodynamic, and environmental parameters that define the built asset’s response to external stressors across its lifecycle [14]. Structural attributes include material properties, geometry, section capacities, joint configurations, and load-path continuity. Thermodynamic attributes focus on heat transfer coefficients, thermal mass, and temperature-dependent degradation patterns. Environmental attributes encompass exposure categories, microclimatic conditions, and local hazard intensities. Through semantic enrichment, these attributes are embedded within BIM elements using standardized schemas to enhance machine interpretability and computational consistency [15]. This structured enrichment ensures that downstream hazard simulations, sustainability analyses, and optimization modules operate on harmonized, granular, and assetspecific datasets. Ultimately, BIM becomes both a geometric repository and a semantically intelligent data source capable of supporting integrated resilience computations [16]. 3.1.2 Climate-stress scenario modeling and hazard parameterization Climate-stress scenario modeling involves projecting hazard intensities such as extreme heat, wind gusts, precipitation surges, and compound climate events across probabilistic time horizons [17]. Hazard parameterization translates these projected stressors into input variables for resilience evaluation, using environmental datasets, climate models, and site-specific exposure profiles. BIM serves as the anchor point by linking hazard parameters directly to corresponding asset components, enabling localized simulation of hazard propagation across structural and thermodynamic systems [18]. Scenario modeling further incorporates frequency–severity relationships, cascading hazard sequences, and seasonal variability, ensuring realistic characterization of hazard dynamics. By embedding these scenarios within BIM, parameterized hazards become computational drivers that systematically inform resilience indices and sustainability-weighted decisions across diverse project conditions [19]. 3.2 Mathematical formulation of multi-hazard resilience indices 3.2.1 Hazard load functions and vulnerability curves Hazard load functions define the intensity and directionality of external forces acting on structural and environmental systems during climate-stress events [20]. These functions describe wind pressure distributions, thermal gradients, accumulated rainfall loads, or hydrodynamic forces, as appropriate for each hazard type. Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [167] Vulnerability curves translate these loads into expected performance degradation, typically depicting nonlinear relationships between hazard magnitude and damage probabilities. They incorporate thresholds for elastic behavior, onset of yielding, progressive damage, and system failure [21]. Within a BIM-integrated workflow, each curve is mapped to specific asset components, enabling granular vulnerability assessment across the entire digital model. Multi-hazard conditions require combining several vulnerability curves through interaction models that account for concurrent or sequential loading effects. This coupling allows the resilience formulation to capture compounding hazard impacts on operational and structural performance [22]. 3.2.2 Construction of composite resilience scoring functions Composite resilience scoring functions synthesize hazard load functions, vulnerability curves, and system performance criteria into unified quantitative indices capable of comparing alternative design or intervention strategies [23]. Each scoring function aggregates multiple hazard contributions through weighted or normalized terms that represent relative hazard significance, exposure duration, or probability of occurrence. Componentlevel resilience scores derive from expected performance drops under hazard-induced stress, while system-level scores reflect aggregated behavior across interconnected subsystems. The scoring function may include performance objectives such as structural robustness, energy continuity, envelope integrity, and recovery time [22]. Through BIM integration, each term draws directly from model-linked data attributes, ensuring asset-specific precision. Multi-hazard scoring requires reconciling simultaneous contributions from thermodynamic, hydrologic, and aerodynamic stresses, requiring harmonization of units, scales, and probabilistic assumptions. The result is a comprehensive resilience index that quantifies how a construction asset withstands projected hazards while maintaining acceptable operational levels. Such indices serve as foundational metrics for sustainability-weighted decision-making and optimization routines [24]. 3.3 Sustainability-weighted decision optimization engine 3.3.1 Weight modelling using multi-objective optimization theory Weight modeling establishes dynamic sustainability weighting factors that respond to hazard intensity, project priorities, and lifecycle objectives [16]. Multi-objective optimization theory provides the mathematical foundation, enabling simultaneous balancing of performance metrics such as embodied carbon, resource efficiency, operational energy, resilience score, and cost. Pareto-front analysis identifies non-dominated solutions, defining trade-off boundaries where improved resilience may conflict with sustainability objectives. Weight functions adjust as hazard scenarios shift, ensuring that sustainability remains meaningful even under extreme or evolving climate conditions [18]. By embedding these adaptive weights into the decision engine, BIM-driven simulations can rank intervention strategies based on their ability to optimize competing objectives holistically. This ensures that sustainability metrics are integrated as active, rather than peripheral, decision drivers. 3.3.2 Trade-off quantification and scenario-adaptive prioritization Trade-off quantification calculates how resilience-improving interventions influence sustainability outcomes and vice versa, ensuring holistic evaluation across multi-hazard conditions [19]. This includes assessing how modifications such as structural reinforcement, envelope upgrades, or material substitutions affect embodied impacts, operational efficiencies, and long-term resource consumption. Scenario-adaptive prioritization ranks alternatives differently depending on hazard probability, exposure severity, or sustainability constraints. For instance, under extreme heat scenarios, thermodynamic resilience may receive higher weighting, while under flood-risk conditions, hydrologic resistance becomes dominant. BIM supports this process by generating scenariospecific datasets for each intervention, allowing the optimization engine to compute alternative outcomes efficiently. The result is a decision-support mechanism that selects strategies consistent with both resilience objectives and sustainability priorities, producing adaptable recommendations matched to project-specific and climate-specific contexts [21]. Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [168] Table 1. Summary of variables, parameters, and computational elements used in index formulation Symbol / Element Description Category Role in Resilience Index Computation 𝐻𝑖 Hazard intensity for scenario i (heatwave, wind, flood) Hazard Parameter Inputs to load functions and scenario modeling; drives stress profiles. 𝐿𝑖(𝑥) Hazard load function applied to component x Load Function Converts hazard intensity into applied forces or thermodynamic gradients. 𝑉𝑖(𝑥) Vulnerability curve for component x under hazard i Vulnerability Element Maps load magnitudes to expected performance degradation. 𝐷𝑖(𝑥) Damage ratio for component x under hazard i Damage Metric Captures degradation severity; computed using vulnerability curves. 𝑅𝑘 Resilience sub-component (robustness, adaptability, redundancy, recovery) Resilience Component Forms the basis for composite resilience scoring functions. 𝑊𝑖 Weight assigned to hazard i Aggregation Parameter Reflects scenario likelihood, severity, and strategic importance. 𝑆𝑗 Sustainability indicator for strategy j (embodied carbon, energy, water, circularity) Sustainability Metric Inputs to multi-objective optimization and trade-off modeling. 𝛼𝑗 Sustainability weighting coefficient for indicator j Optimization Weight Governs influence of sustainability metrics on final resilience index. 𝐶(𝑥) Composite resilience score for component x Resilience Index Term Aggregates hazard-specific resilience components into unified output. Φ Multi-hazard interaction factor Interaction Parameter Adjusts resilience score for compounding or cascading hazard effects. 𝑇𝑠 Time-dependent scenario parameter Scenario Variable Models temporal variation in hazard exposure and degradation. BIM-Attribute Set Geometry, material properties, thermodynamic attributes, environmental metadata Data Source Provides all computational inputs; ensures model-to-reality alignment. Simulation Engine ℰ Hazard and performance simulation module Computational Component Generates load responses, stress paths, thermal fields, and hydrologic impacts. Optimization Engine 𝒪 Sustainability-weighted decision model Computational Component Computes scenario-adaptive weighting and selects optimal intervention strategies. Normalization Operator 𝒩 Unit and scale harmonization Mathematical Operation Ensures cross-hazard comparability among metrics. Aggregation Operator Σ Multi-criteria synthesis of resilience components Mathematical Operation Produces final resilience index at component and system levels. 4. BIM-INTEGRATED COMPUTATIONAL WORKFLOW 4.1 Workflow architecture linking BIM, hazard simulation, and sustainability metrics The workflow architecture integrates BIM, hazard simulation engines, and sustainability assessment modules into a unified computational environment capable of supporting real-time, multi-hazard resilience evaluation [21]. At its core, BIM functions as the data backbone, housing geometric, material, thermodynamic, and environmental attributes enriched through semantic classification. These attributes serve as the initial inputs for hazard simulation Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [169] modules, which compute climate-stress responses such as thermal gradients, wind pressures, hydrological loads, and their propagation through structural and envelope systems [22]. The workflow ensures bidirectional connectivity: hazard modules retrieve BIM-linked parameters while updated hazard outcomes are written back to BIM objects for iterative evaluation. Parallel to hazard modeling, sustainability metrics embodied carbon, lifecycle energy demand, water footprint, and circularity contributions are computed using data streams connected to BIM datasets [23]. Integrating these dimensions into a shared architecture allows hazard and sustainability components to interact during decision analysis, preventing contradictory recommendations. Middleware interfaces facilitate synchronization across simulation tools, ensuring that scenario variations or design updates propagate automatically through all computational layers [25]. This fusion of data streams establishes a cohesive resilience–sustainability ecosystem capable of generating both component-level and systemwide performance insights. Ultimately, the architecture provides the structural foundation for robust, BIM-centered resilience index generation [27]. 4.2 Algorithmic pipeline for multi-hazard resilience index generation The algorithmic pipeline transforms BIM-enriched datasets into quantitative resilience indices through a staged computational sequence designed to evaluate hazard interactions and sustainability influences with high precision [24]. The first stage extracts hazard-relevant attributes from BIM and standardizes them through normalization processes that align units, scales, and probabilistic assumptions. These data feed into hazard simulation engines, which compute scenario-specific load functions reflecting wind, heat, flood, and compound hazard intensities. Simulation outputs then activate vulnerability assessment modules, where component-level damage states and performance degradation rates are computed using calibrated vulnerability curves linked to BIM elements [26]. The second stage synthesizes hazard-specific degradation outputs into multi-hazard response profiles. These profiles incorporate temporal dynamics, hazard concurrency, and cascading failure likelihoods to construct a combined response model reflective of realistic climate stress behavior [28]. This multi-hazard response is translated into resilience components such as robustness, rapidity, redundancy, and adaptability. Each component is measured numerically and aggregated through weighted scoring functions grounded in system performance objectives. The third stage incorporates sustainability-weighted adaptations by adjusting weighting parameters based on embodied carbon limits, energy performance thresholds, or environmental constraints embedded within BIM datasets. A multi-objective evaluation compares candidate interventions by examining their influence on resilience scores and sustainability indicators simultaneously, producing a final resilience index for each scenario [29]. The pipeline culminates with automated index reporting, where results are parameterized based on hazard intensity, asset configuration, and sustainability priorities. This end-to-end algorithmic flow ensures transparency, reproducibility, and computational rigor essentials for integrating resilience analytics into construction engineering management practices [30]. 4.3 Integration with decision-support interfaces and visualization dashboards Decision-support interfaces and visualization dashboards transform resilience indices into actionable insights that guide engineering, design, and project management decisions [23]. These interfaces connect directly to BIM databases and analysis engines, allowing users to interact with real-time scenario outputs. High-resolution dashboards visualize hazard propagation patterns, vulnerability hotspots, and component-level resilience scores using heat maps, structural overlays, and time-based performance trajectories [21]. Sustainability metrics appear alongside resilience results, enabling comparison across alternative interventions and highlighting trade-offs that must be negotiated to meet project-specific objectives. Interactive modules allow stakeholders to adjust hazard intensities, sustainability weightings, or design parameters and instantly observe resulting index changes. This adaptive capability enhances decision transparency by linking each user adjustment to explicit computational outcomes derived from the underlying algorithmic pipeline [24]. Automated reporting tools generate scenario summaries, resilience–sustainability balance sheets, and recommended intervention sequences tailored to specific priorities such as carbon reduction, lifecycle extension, or enhanced robustness [27]. The interface architecture supports multi-user collaboration, enabling engineers, sustainability consultants, and project managers to explore resilience strategies concurrently through synchronized views. Integrated alerts highlight conditions where resilience improvements compromise sustainability goals, or where sustainability gains significantly reduce hazard resistance. These warnings ensure that decisions remain aligned with broader project values and regulatory contexts [25]. Finally, the dashboards integrate predictive analytics modules capable of forecasting future resilience performance under evolving climate projections, allowing long-term planning within uncertain hazard environments. Through Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [170] seamless connectivity with BIM and computational engines, the visualization system becomes a strategic tool for guiding climate-adaptive construction practices that are both resilient and sustainable. Figure 2: BIM-integrated workflow diagram, showing data flows from hazard simulation to resilience scoring. Table 2. Mapping of BIM-Based Data Sources to Hazard–Sustainability Evaluation Modules BIM Data Category Specific Data Elements Extracted Hazard Evaluation Module Utilized Sustainability Evaluation Module Utilized Function in Integrated Workflow Geometric Data Component dimensions, spatial coordinates, façade geometry, roof profiles Wind-pressure simulation, rainfall accumulation odelling, flood pathway mapping Material quantity estimation for embodied carbon Determines exposure surfaces and hazard interaction zones; drives carbon and material calculations. Structural Attributes Material strengths, stiffness matrices, load paths, connection details Structural load propagation, nonlinear stress assessment, failure mode prediction Longevity evaluation, maintenance frequency modeling Defines hazard-induced damage progression and lifecycle durability. Thermodynamic Properties Thermal conductivity, heat capacity, insulation layers Heatwave response simulation, thermal stress modeling Building energy demand calculations, HVAC sustainability metrics Couples thermal resilience with operational energy performance. Environmental Metadata Elevation, microclimate data, surrounding topography Flood depth estimation, windchanneling effects, microclimate-driven heat intensification Local environmental impact assessment Aligns hazard intensity with site conditions and sustainability baselines. Material Specifications Embodied carbon factors, recyclability, lifecycle degradation coefficients Material deterioration under hydrological and thermal stress Carbon footprint odelling, circularity index computation Evaluates trade-offs between reinforcement strategies and sustainability impacts. Volume-09 Issue 12, December -2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [171] BIM Data Category Specific Data Elements Extracted Hazard Evaluation Module Utilized Sustainability Evaluation Module Utilized Function in Integrated Workflow Operational Profiles Occupancy schedules, internal heat generation, equipment loads Thermal load amplification analysis during heatwaves Operational energy consumption and peak demand impacts Integrates dynamic usage conditions into hazard and sustainability odelling. Drainage & Utility Systems Catchment areas, pipe diameters, pump capacities Flood routing analysis, overflow risk assessment Water efficiency projections, longterm utility performance Supports flood resilience evaluation and waterrelated sustainability metrics. Maintenance & Lifecycle Records Historical repair patterns, degradation logs Prediction of failure frequency and hazardsensitive deterioration Lifecycle cost and carbon estimation Enables predictive resilience performance and sustainability benchmarking. Semantic Classifications Vulnerability tags, exposure categories, sustainability labels Hazard-prioritized component ranking Sustainabilityweighted decision rules Provides automationready metadata for integrated scoring and optimization. 5. CASE STUDY DEMONSTRATION 5.1 Project description and BIM dataset characteristics The case study evaluates a mid-rise healthcare facility situated within a rapidly urbanizing, climate-sensitive region exhibiting increasing exposure to compound environmental hazards [27]. The project’s BIM model includes comprehensive structural, architectural, thermodynamic, and operational datasets developed to Level of Development (LOD) 350, enabling fine-grained attribute extraction. Structural components columns, slabs, shear walls, and façade systems are parameterized with material strengths, stiffness profiles, and degradation curves. Thermodynamic elements, such as insulation layers and thermal masses, are represented using embedded conductivity and heat-capacity values [28]. Environmental metadata integrates site elevation, historical hazard frequencies, projected climate intensities, and proximity to critical infrastructure. The BIM model is semantically enriched through classification schemas that tag components with vulnerability categories, hazard exposure coefficients, and sustainability attributes such as embodied carbon factors and predicted operational energy consumption [29]. This enriched BIM dataset serves as the analytical foundation for hazard propagation simulations and sustainability-weighted decision modeling. Its completeness ensures that resilience indices reflect the true asset configuration rather than abstract or generalized conditions. The case study thus demonstrates how BIM-centric data structuring enables precise, scenario-adaptive resilience and sustainability assessments aligned with contemporary construction engineering demands [30]. 5.2 Climate-stress scenarios applied: heatwaves, extreme winds, flooding Three primary climate-stress scenarios were applied to evaluate the facility’s resilience under plausible future environmental conditions: sustained heatwaves, extreme wind events, and localized flooding. These scenarios align with regional climate projections indicating rising temperatures, intensifying wind patterns, and more frequent extreme rainfall occurrences [31]. Heatwave simulations computed thermal gradients across envelope systems, capturing the interaction between solar gains, occupancy loads, and heat island amplification. Prolonged high-temperature exposure was modeled to assess thermal fatigue, material expansion, and HVAC overload risks, all of which affect resilience and operational continuity. Extreme wind simulations incorporated aerodynamic profiles derived from local topographic conditions and building orientation. Pressure coefficients were applied to façade systems and roof assemblies using BIM-linked geometries, enabling realistic modeling of suction forces, vortex shedding, and potential envelope breaches [27]. Flooding simulations evaluated surface runoff dynamics, infiltration pathways, drainage network performance, and subgrade impacts. Hazard depth-duration curves were mapped to BIM-embedded elevations and foundation components, identifying zones most susceptible to water-related damage. Scenario interactions were also considered, accounting for cascading effects such as wind-induced debris damage aggravated by heat-weakened materials or flooding intensified by storm-driven drainage failures [32]. Together,