Traditional machine learning and deep learning models require significant training data, limiting their applicability in new or data-scarce environments. To overcome this, a symbolic regression-based approach is presented to develop a data-efficient and interpretable forecasting model for cooling loads. Equipment and weather data collected from a large hospital is used for training and validation, demonstrating the model's effectiveness in minimizing data dependency and addressing cold start challenges. The proposed framework outperforms traditional black-box models in terms of interpretability, data efficiency, and adaptability, making it suitable for real-time deployment and broader energy management applications.
Geometric Digital Twins offer a robust framework for managing road environments by integrating high-fidelity object geometries and their interrelationships. However, existing relationship modelling approaches rely on manual processes or are not tailored for road objects, creating bottlenecks in large-scale digitisation. This paper proposes a hybrid method combining spatial analysis with large language models to automate topological and functional relationship modelling among road assets. Our approach captures and enriches the spatial context with semantic insights derived from relevant textual data. The resulting holistic GDT leads to more efficient information and asset management, facilitating advanced use cases, including safety assessments and simulations.
Accurate early-stage cost forecasting in renovation projects helps property owners make informed decisions, conserving resources and enhancing efficiency. However, estimates are challenging due to project complexity. Traditional methods, often intuitive and dataset-limited, lack precision. This study analyzes 104 projects using decision trees, finding that regression achieves a lower mean absolute percentage error (MAPE) of 7 %, versus 15 % for classification. A combined approach, integrating external data from Google Earth and Google Solar API, improves accuracy and traceability, emphasizing the potential for reliable, data-driven decisions in renovation projects.
The European Energy Performance of Buildings Directive (EPBD) promotes Building Renovation Passports (BRPs) but faces challenges from inconsistent Energy Performance Certificate (EPC) data and high onsite assessment costs. This study develops a digital Renovation Feasibility Assessment Tool using building archetypes, open data, and spatial analysis to automate early-stage feasibility evaluations. A case study of nine buildings shows its effectiveness in assessing renovation options, including district heating transitions and photovoltaic (PV) potential. By integrating scalable digital solutions, the tool enhances BRP accessibility and supports informed decision-making for sustainable renovations.
This paper introduces a regenerative mindset from Regenerative Agriculture (RA) that fosters robust reuse of information in the Architecture, Engineering, Construction, and Facility Management (AEC/FM) sector. By stacking enterprises and forging virtuous cycles, RA principles highlight synergy, resilience, and resource efficiency. Drawing on engaged scholarship, we demonstrate how outcome-driven data use addresses longstanding implementation barriers by centering on virtuous cycles rather than isolated technological inputs. We propose a new management paradigm that elevates data reuse, expediting meaningful digital transformation while advancing environmental targets.
Construction procurement remains inefficient, paper-based, and lacks transparency. Current systems fail to ensure reliable bid evaluations and sustainability compliance, particularly in waste management. This research proposes a blockchain-based framework integrating BIM, Smart Contracts and process normalization to automate tender evaluations, ensuring tamper-proof data storage and verifiable assessments. A prototype was tested through an Italian Design-Build procurement. Results highlight enhanced compliance monitoring and fairer bid selection based on sustainability criteria. By establishing standardized processes for data submission and verification, this approach fosters trust and promotes digital transformation in public procurement, setting a new standard for transparency and waste management.
