Search Results (8)

The construction sector accounts for around 40% of global energy usage and surpasses 36% of carbon emissions, highlighting the urgent need for improved renovation strategies. This research presents an AI-enabled generative design optimization framework that facilitates concurrent multi-objective optimization of architectural design, structural efficiency, and energy performance. The framework employs a 20-variable parametric design space and integrates a hybrid NSGA-III, a reference-point-based many-objective evolutionary algorithm with particle swarm optimization. Machine-learning surrogate models accelerate physics-based simulations by 500–850 times while maintaining prediction accuracy above 95%. The framework is validated through twelve renovation case studies comprising eleven residential and one office building spanning seven European countries, sourced from IEA SHC Task 37 and Passivhaus Institut databases, and calibrated to ASHRAE Guideline 14 standards (CVRMSE ≤ 18.6% across all buildings). The results evidence average reductions of 84.7% in operational energy consumption and enhancements of 20.1% in material efficiency, while consistently attaining a net-zero annual energy balance. Climate conditions significantly influence optimal insulation requirements, with a 37% difference between continental and Mediterranean regions. This study presents a scalable and computationally efficient method for AI-driven renovation design, overcoming the constraints of sequential approaches and facilitating substantial decarbonization of the built environment. Full article

The refurbishment and energy-efficient renovation of existing buildings, specifically those before 1945, with complex architectural building elements, require a level of building information that is often unavailable, incomplete or imprecise. As they represent a large percentage of current building stock (up to 25% of all buildings), it is crucial to address such an issue, as such buildings are ideal targets for renovation and energy retrofitting projects. This paper presents a conceptual pipeline, developed through the NERF2BIM research project, focusing on an Artificial Intelligence (AI)-supported holistic pipeline for the creation of as-is Building Information Modeling (BIM) models of existing buildings, expanding upon existing methodologies with the embedding of knowledge-driven semi-automatic detailing-on-demand task. The proposed pipeline integrates uncertainty-aware spatial capture, semantic interpretation and reconstruction, and knowledge-based reasoning within a BIM-oriented workflow. The paper provides an overview of current advancements in the respective aspects of the pipeline, highlighting current gaps. The proposed conceptual pipeline aims at addressing these issues through novel applications of AI and knowledge-driven solutions. Key contributions include: (1) a conceptual approach in addressing the imprecision of more sustainable data gathering approaches; (2) a context-aware BIM reconstruction process, providing multiple data output types; and (3) a formalization of architectural and construction knowledge and its utilization in a detailing-on-demand approach of the reconstructed BIM models. Through the integration of uncertainty-aware data gathering, context-aware reconstructions and domain expertise into existing reconstruction pipelines, the proposed pipeline bridges the data gap for existing buildings, enabling more efficient and knowledge-driven renovation processes. Full article

The Italian school building stock is largely outdated, with structural and technological inadequacies leading to low comfort and high energy consumption. Addressing this challenge requires large-scale renovation supported by an integrated, data-driven approach. This study conducted a nationwide analysis of over 40,000 school buildings. After incomplete or inconsistent records were filtered out, a refined subset was selected. Building forms were reconstructed by cross-referencing GIS data with multiple open data sources. Using supervised machine learning, the research identifies and classifies recurring morphological patterns to define a set of 3D school building archetypes. These archetypes are enriched with spatial configurations and physical characteristics aligned with national educational standards. The result is a macrotypological classification based on form, conceived as part of an operational tool to support policymakers, designers, and public administrations in selecting effective retrofit strategies. This contributes to the creation of large-scale national renovation strategies, as well as Renovation Roadmaps and Digital Building Logbooks in line with the Energy Performance of Buildings Directive (EPBD IV), specifically tailored to the Italian context. The novelty of this work lies in its unprecedented scale and the use of AI to enable fast, replicable assessments of retrofit potential, thereby supporting informed decisions in energy-efficient renovation planning. Full article

The European building stock is aging and needs renovation. Holistic renovation approaches, including Vertical Forest (VF) systems, are emerging as sustainable alternatives to demolition and reconstruction. This paper reviews and defines missing reliable damage and hazard intensity measures for the holistic renovation of existing reinforced concrete (RC) buildings with VF systems. Based on an extensive literature review and preliminary studies, including empirical multiparametric system evaluation assessments, Monte Carlo simulations, and System-Theoretic Process Analysis (STPA), combined structural, non-structural, vegetation, and human comfort components are examined. Key damage indicators are identified, including interstory drift ratio, residual deformation, concrete and reinforcement strains/stresses, and energy dissipation, and their applicability to VF-integrated structures are evaluated. Green modifications are found to have higher risk profiles than traditional RC buildings (mean scores from Monte Carlo method: 9.72/15–11.41/15 vs. 9.47/15), with moisture management and structural integrity as critical concerns. The paper advances the understanding of hazard intensity measures for seismic, wind, and rainfall impacts. The importance of AI-driven vegetation monitoring systems with 80–99% detection accuracy is highlighted. It is concluded that successful VF renovation requires specialized design codes, integrated monitoring systems, standardized maintenance protocols, and enhanced control systems to ensure structural stability, environmental efficiency, and occupant safety. Full article

The application of artificial intelligence (AI) in tall buildings’ development provides transformative opportunities for facing population growth pressures and sustainability challenges in cities. This study presents a comprehensive review of both the current literature and the theoretical framework of AI and its role in construction, specifically analyzing the convergence of AI and skyscraper development. The research methodology combines scholarly sources, AI image generation techniques, an analytical approach, and a comparative analysis of traditional versus AI-enhanced approaches. This study identifies key domains where AI significantly impacts skyscraper evolution, including design optimization, energy management, construction processes, and operational efficiencies. It highlights short-term benefits like enhanced architectural design through rapid generative design iterations and material optimization, alongside long-term implications involving adaptive building technologies and sustainability enhancements. Additionally, it addresses the advantages and challenges of adopting AI in architecture, considering various factors (e.g., sustainability, security, and occupant well-being), as well as the impact of different climates on AI in architecture and construction. It also explores transformative applications across diverse skyscraper functions and how AI can bridge different cultures and technologies. The findings reveal AI’s substantial potential in TBs’ design and management, (i.e., structural optimization, energy saving, safety protocols, and operational efficiency) by leveraging innovative technologies such as machine learning, computer vision, and predictive modeling. In conclusion, AI’s dual role as both a revolutionary tool that enhances traditional architectural methods and a catalyst for new design paradigms prioritizing sustainability and resilience has been reflected. Ultimately, this research underscores the importance of balancing AI innovation with established architectural principles to foster a favorable urban future that embraces both technological advancement and foundational design values. This study serves as a base for future research in the AI field. Full article

This paper explores the applications and impacts of artificial intelligence (AI) in building envelopes and interior space design. The relevant literature was searched using databases such as Science Direct, Web of Science, Scopus, and CNKI, and 89 studies were selected for analysis based on the PRISMA protocol. This paper first analyzes the role of AI in transforming architectural design methods, particularly its different roles in the auxiliary, collaborative, and leading design processes. It then discusses AI’s applications in the energy-efficient renovation of building envelopes, smart façade design for cold climate buildings, and thermal imaging detection. Furthermore, this paper summarizes AI-based interior space environment design methods, covering the current state of research, applications, impacts, and challenges both domestically and internationally. Finally, this paper looks ahead to the broad prospects for AI technology in the architecture and interior design fields while addressing the challenges related to the integration of personalized design and environmental sustainability concepts. Full article

The challenge of high energy consumption and carbon emissions within China’s construction industry has become increasingly urgent, as over 40% of buildings are still non-energy efficient. The multifaceted nature of systems involved in building retrofits results in a complex project, with barriers in both retrofit design and construction becoming increasingly evident. This research comprehensively assesses the common barriers in building retrofits and investigates the potential for integrating energy-efficient retrofits with information flow modeling from an interdisciplinary perspective. In order to pinpoint the main barriers hindering building retrofits, this study employs the bibliometric software VOSviewer. The analysis uncovers that the primary obstacles to energy-saving renovations are categorized into technical, economic, environmental, and other barriers. These barriers are characterized by a high degree of specialization, the inadequate integration of information, and limited collaboration among stakeholders. Subsequently, a qualitative literature review was conducted following the PRISMA methodology, which screened 40 key sources. The following conclusions were drawn: (1) The design of energy-saving renovation processes is impeded by the limited professional perspectives within the construction industry, which restricts the practical applicability; (2) Decision making for energy-saving renovations encounters notable professional barriers and suffers from inadequate information integration; (3) There is a lack of clarity regarding information needs during the implementation phase, and no effective platform exists for information coordination; (4) Risk analyses in complex energy-saving renovations largely depend on expert interviews, lacking robust scientific tools. These findings highlight that knowledge gaps and information asymmetry are the central challenges. To tackle these issues, this paper suggests the implementation of an information flow model that integrates the IDEF0 and DSM for building energy-saving retrofit projects. The IDEF0 model can clearly describe the interaction relationship of all expert information through functional decomposition, while the DSM can show the dependency relationship and information flow path among specialties through the matrix structure. This model is anticipated to enhance professional information integration and collaboration. It is proposed that improved information integration and collaboration under this framework will significantly promote the advancement of professional generative AI. Full article

This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the academic world. With the necessary improvement of energy efficiency, discernment is paramount for facility managers to optimize daily operations and prioritize renovation work in the building sector. With the scale of buildings and the complexity of Heating, Ventilation, and Air Conditioning (HVAC) systems, the use of artificial intelligence is deemed the cheapest tool, holding the highest potential, even if it requires IoT sensors and a deluge of data to establish genuine models. However, the wide variety of buildings, users, and data hinders the development of industrial solutions, as specific studies often lack relevance to analyze other buildings, possibly with different types of data monitored. The relevance of the modeling can also disappear over time, as buildings are dynamic systems evolving with their use. In this paper, we propose to study the forecasting ability of the widely used Long Short-Term Memory (LSTM) network algorithm, which is well-designed for time series modeling, across an instrumented building. In this way, we considered the consistency of the performances for several issues as we compared to the cases with no prediction, which is lacking in the literature. The insight provided let us examine the quality of AI models and the quality of data needed in forecasting tasks. Finally, we deduced that efficient models and smart choices about data allow meaningful insight into developing time series modeling frameworks for smart buildings. For reproducibility concerns, we also provide our raw data, which came from one “real” smart building, as well as significant information regarding this building. In summary, our research aims to develop a methodology for exploring, analyzing, and modeling data from the smart buildings sector. Based on our experiment on forecasting temperature sensor measurements, we found that a bigger AI model (1) does not always imply a longer time in training and (2) can have little impact on accuracy and (3) using more features is tied to data processing order. We also observed that providing more data is irrelevant without a deep understanding of the problem physics. Full article