Aug 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XLIX-B4-2026, pp. 507-512· 0 citations· 6 references
Abstract
Abstract. Artificial Intelligence (AI) integration has become an essential of modern AEC workflows, yet it has failed to gain a position in waste management. This gap is particularly prominent given the urgent environmental and legal imperatives for the sector to mitigate its demolition outputs. Existing approaches to waste classification and diversion cost estimation rely on manual interpretation of project documentation, a process that is both resource-intensive and structurally incompatible with the machine-readable data environments established by Building Information Modelling (BIM). This paper presents a framework that bridges Industry Foundation Class (IFC) compliant BIM data and Large Language Model (LLM) capabilities to automate Construction and Demolition Waste (C&DW) classification and probabilistic cost optimisation. The framework utilizes IfcOpenShell to extract element geometry and material data, channeling this information into a Retrieval-Augmented Generation (RAG) pipeline. To ensure rigorous compliance during classification, a FAISS-indexed knowledge base grounds a locally deployed Llama3 model against the specific mandates of Province of Ontario, Canada regulation 102/94. Diversion cost scenarios are computed through a Bayesian cost module coupled to a multi-objective genetic algorithm (MOGA) optimiser. Th proposed approach is evaluated against a labelled dataset of 104 IFC type-and-material combinations, the RAG classifier. Performance thresholds were established a piori based on multi-class classification benchmarks and Bayesian cost model uncertainty tolerances. The framework achieved a macro-average F1 of 0.84 and overall accuracy of 88%, satisfying the minimum criteria for automated C&DW characterization under Ontario Regulation 102/94.
Abstract. Building Information Modelling (BIM) is increasingly mandated worldwide as part of the construction industry's digital transformation. While widely used in design and construction, its potential for managing construction and demolition waste (C&DW) remains underexplored, despite demolition accounting for 70–90% of building-related waste and 30–40% of global solid waste. BIM Revit native models provide rich data but are computationally intensive and require specialist expertise, limiting their direct use for waste quantification and sustainability evaluation. This paper develops a BIM-enabled data integration and visualization framework that automates waste estimation, material classification, and environmental evaluation by linking BIM data with heterogeneous datasets through Speckle connectors and Power BI dashboards. Supplementary datasets included material densities, expansion coefficients, recycling rates, and environmental factors such as CO2 emissions and energy intensities. A case study of York University’s Bergeron Centre illustrates the framework’s effectiveness across three demolition stages. The non-invasive dismantling phase highlighted significant opportunities for material recovery, while semi-invasive deconstruction captured recyclable structural components with moderate landfill requirements. The final core demolition stage revealed the greatest potential for recycling, particularly in concrete and steel, though it also underscored the challenges of diverting large volumes of residual waste from disposal. By integrating BIM with environmental datasets and interactive dashboards, the system delivered holistic insights into recovery, landfill diversion, and CO2 reduction. Findings confirm its scalability, accessibility, and value as a decision-support tool for sustainable demolition and circular economy objectives.
Sara Abbasian, M. Jadidi· The International Archives o...· 0 citations
The increasing complexity of construction projects has made traditional planning methods inadequate for managing dynamic variables. In this context, the integration of building information modelling (BIM) and artificial intelligence (AI) has been increasingly investigated as a promising approach to improve estimation accuracy, decision-making, and sustainable project execution. This systematic literature review, conducted according to the PRISMA guidelines, analysed 47 articles on BIM-AI integration for construction cost and time planning, categorising them into three clusters: time-oriented, cost-oriented, and multi-objective planning. The reviewed studies indicate that BIM-AI workflows may improve planning efficiency, estimation accuracy, and resource allocation. A limited subset of studies directly incorporated energy consumption, carbon emissions, or lifecycle performance into the optimisation objectives. By contrast, broader benefits related to material waste, rework, equipment idle time, and safety were mainly inferred from improvements in scheduling and resource management rather than directly quantified across the reviewed studies. Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) emerged as the most frequently adopted and consistently reported techniques, while Revit was the most adopted BIM platform. Despite its potential, BIM-AI integration still faces challenges related to software interoperability, data quality, interdisciplinary coordination, and the limited integration of explicit sustainability indicators within optimisation models. Future research should focus on developing standardised and adaptable frameworks that jointly address cost, time, resource efficiency, environmental impact, and lifecycle performance across different construction contexts.
Serena Vitaliano, S. Cascone, C. Arcidiacono· Sustainability· 0 citations
This framework proves an autonomous decision-making system that organically links inspection data with maintenance regulations by transforming static, manual-labor-centered maintenance workflows into intelligent automated models and increases the efficiency of railway infrastructure management while providing a scalable technical foundation for overall asset management of future smart-city infrastructure.
Minjae Jeon, Yong-Gyun Kim, Seok-Han Kim· Smart Cities· 0 citations
The integration of temporary support systems with permanent structural frameworks remains a complex challenge in high-rise construction, where traditional building information modeling (BIM) workflows often lack the coordination needed to optimize both simultaneously. This study addresses that gap through a federated BIM workflow using Tekla Structures, Revit, Navisworks, and Dynamo on a fourteen-story building with a five-level basement. The temporary shoring, permanent concrete frame, and formwork were modeled at Level of Development (LOD) 300–350. A parameter-driven methodology in Dynamo managed large-scale clash detection. Steel cutting-list optimization reduced sheet-cutting waste from 4.5% to below 3% across 18 tons of steel. Clash detection between the truss guard and permanent rebar initially returned over 6000 conflicts; categorizing these via Dynamo made resolution tractable. This data optimized plywood formwork, reducing requirements by 15% (3350 to 2850 square meters), and refined rebar scheduling, recovering 51 of 103 additional tons, 32.8 tons of which are directly attributable to the truss-rebar interaction-zone workflow presented in this study. The study demonstrates that federating temporary and permanent models generates reusable coordination data, yielding savings beyond simple clash avoidance. It offers a transferable workflow for temporary-to-permanent structural integration that the current literature has not yet examined quantitatively.
Mohammadsoroush Tafazzoli, Sahar Falegari, A. A. Shirzadi Javid· Buildings· 0 citations
This study aims to develop and validate a building information modeling (BIM)-based automation framework that integrates the Estidama Pearl Rating System (PRS) directly into Autodesk Revit. The research focuses on the Materials category, addressing the industry’s reliance on manual documentation and static assessment methods. It seeks to enable real-time sustainability evaluation and enhance accuracy, efficiency and decision-making during the design phase of residential projects in the United Arab Emirates.
A partially automated Revit-based framework was developed using parameterized data structures representing Estidama’s Materials subcredits. These parameters are dynamically linked with Dynamo automation scripts for rule-based calculations and credit scoring, while One Click Life Cycle Assessment (LCA) integration provides embodied carbon and environmental impact data consistent with Estidama’s requirements. The system was validated through a three-story residential villa in Dubai, testing feasibility, accuracy and responsiveness under actual design conditions.
The framework partially automated measurable subcategories of Estidama’s Materials section, reducing manual effort by approximately 90% and improving calculation accuracy to within a 1% error margin. Dynamic linkage between model parameters and sustainability metrics allowed real-time recalculation of scores following design modifications. The results confirm that BIM can function as an intelligent sustainability engine, transforming Estidama assessment into a proactive design-stage process.
The framework currently depends on accurate manual entry of supplier and material data. Future enhancements could integrate automated data sourcing and expand applicability to other rating systems, including Leadership in Energy and Environmental Design and Building Research Establishment Environmental Assessment Method.
By automating Estidama assessments, the framework minimizes consultant workload, reduces reporting time and ensures consistent compliance, supporting faster project approvals and improved sustainability outcomes.
The study promotes climate-responsive construction aligned with the UAE’s Net Zero 2050 strategy by encouraging wider adoption of digital sustainability assessment practices in the Gulf region.
This research is the first to automate Estidama PRS scoring within Autodesk Revit using Dynamo and LCA integration. Unlike existing LEED or BREEAM automation frameworks, it provides a region-specific, parameterized and scalable solution tailored to the UAE’s sustainability standards. The study contributes a replicable foundation for future digitalization of local certification systems and advances BIM’s role in data-driven environmental compliance.
P. Youssef, Malek Masmoudi, Ali Cheaitou et al.· Facilities· 0 citations
The buildings and construction sector is one of the largest contributors to global greenhouse gas emissions. It accounts for around 34% of global energy-related CO₂ emissions, which come from both the energy used to operate buildings and the emissions produced when construction materials such as cement, steel and concrete are made. Despite the increasing use of embodied-carbon assessment methodologies in the architecture, engineering and construction (AEC) sector, carbon accounting tools are still not integrated into the cost estimation and procurement processes that directly affect material selection and initial design choices. This study presents the novel, AI-driven 'EEBOQ' framework, which was developed in the context of the Latvian construction market while taking into account broader European and international carbon accounting practices. The proposed framework integrates a hybrid semantic processing pipeline that combines Large Language Models (LLMs), ontology-based classification mechanisms and vector-based similarity retrieval techniques in order to autonomously interpret heterogeneous, spreadsheet-based procurement documentation. The system aligns the free-text estimate positions with standardised embodied-carbon reference datasets, such as the ICE Database, Environmental Product Declarations (EPDs) and EN 15978-compliant life cycle assessment structures. To improve the reliability of practical CO₂ estimation, the framework introduces a Bayesian Feedback Correction Engine (BFCE). This is designed to reduce discrepancies iteratively between generalised look-up table emission factors and observed, project-specific embodied carbon data. The feedback mechanism continuously recalibrates environmental coefficients using primary data provided by suppliers, transport information related to logistics, records of material substitution, and validated environmental product declarations. Experimental validation on a corpus of real-world construction projects demonstrated that the semantic-matching module achieved top-1 matching accuracy of 89.3% and top-3 accuracy of 97.1%. Furthermore, the proposed Bayesian correction mechanism reduced the median absolute percentage error in embodied carbon estimation from 28.5% using a conventional static look-up table to 8.3% after three iterative feedback cycles. The results obtained indicate that the proposed architecture establishes a scalable, reproducible pathway towards real-time, evidence-based embodied carbon accounting that is directly integrated with operational construction cost management and procurement processes.
Oskars Bagants, Raita Rollande, Juris Klonovs· Baltic Journal of Economic S...· 0 citations