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Cities and buildings represent the core of human life, the nexus of economic activity, culture, and growth. Although cities cover less than 10% of the global land area, they are notorious for their substantial energy consumption and consequential carbon dioxide (CO[sub.2]) emissions. These emissions significantly contribute to reducing the carbon budget available to mitigate the adverse impacts of climate change. In this context, the designers’ role is crucial to the technical and social response to climate change, and providing a new generation of tools and instruments is paramount to guide their decisions towards sustainable buildings and cities. In this regard, data-informed digital tools are a viable solution. These tools efficiently utilise available resources to estimate the energy consumption in buildings, thereby facilitating the formulation of effective urban policies and design optimisation. Furthermore, these data-driven digital tools enhance the application of algorithms across the building industry, empowering designers to make informed decisions, particularly in the early stages of design. This paper presents a comprehensive literature review on artificial intelligence-based tools that support performance-driven design. An exhaustive keyword-driven exploration across diverse bibliographic databases yielded a consolidated dataset used for automated analysis for discerning the prevalent themes, correlations, and structural nuances within the body of literature. The primary findings indicate an increasing emphasis on master plans and neighbourhood-scale simulations. However, it is observed that there is a lack of a streamlined framework integrating these data-driven tools into the design process.

The global warming effect represents an increasingly severe environmental issue in the contemporary world, with the construction industry contributing up to 40% of greenhouse gas emissions. Therefore, as advancements in technology have enabled the realization of net-zero energy buildings, there has recently been a growing focus on research primarily aimed at reducing the embodied carbon (EC) of building materials. Assessment and calculation of EC emissions in buildings typically utilize life cycle assessment (LCA) methodologies, evaluating both direct and indirect carbon emissions throughout all stages, from raw material extraction to end-of-life demolition. However, the substantial potential of carbon reduction within the material beyond life cycle stage in the building, which is the decisive process of closing the loop of circular economy, is often overlooked. This paper examines a large number of research cases on EC in buildings over the past 20 years, selectively identifying those including the benefits beyond life cycle of buildings. By conducting a case-by-case analysis of methods and tools employed for the assessment of circular practices, their respective strengths, weaknesses, and variances are evaluated. Following the normalization of EC in phase A-D, a significant research finding revealing that buildings can offset an average of -113.9 kg CO 2e /m 2 of carbon emissions through recycling and reuse in phase D, accounting for 16.85% of the total EC assessed in LCA. Steel recycling offsets the highest amount of carbon emissions, with an average number of -183.86 kg CO 2e /m 2 . The objective of this paper is to identify the key factors that influence carbon emissions in the circular economy and to identify methods and tools for integrating building materials at the early design stage to minimize EC emissions throughout the entire lifecycle of buildings.

The need for energy efficiency in neighborhood-scale architectural design is driven by environmental imperatives and escalating energy costs. This study identifies three key phases in a design process framework where machine learning can be applied to optimize energy consumption in early design stages. The overall framework integrates machine learning tools into the design workflow, enhancing design exploration from concept level and enabling targeted energy assessments. This paper focuses on the first phase (Phase 1) of the framework, which employs machine learning for building energy forecasting using only the few inputs available in a business-as-usual early-stage design workflow. The CatBoost model was selected for its high accuracy in predicting energy consumption using minimal input data. A preliminary application to a case study in New York City showed high predictive accuracy while reducing the input needed, with R2 scores of 0.88 for both cross-validation and test datasets. Shapely additive explanation analysis validated the selection of key influencing parameters such as building area, principal building activity, and climate zones. The test demonstrated discrepancies between the test data-driven model and a physics-based energy model values ranging from −8.69% to 11.04%, which can be considered an acceptable result in early-stage design. The remaining two phases, though outside the scope of this study, are introduced at a conceptual level to provide an overview of the full framework. Phase 2 will analyze building shape and elevation, assessing the total energy use intensity, while Phase 3 will apply district-level energy optimization across interconnected buildings. The findings from Phase 1 underscore the potential of machine learning to integrate energy efficiency considerations into neighborhood-scale design from the earliest stages, providing reliable predictions that can inform sustainable design.

Nowadays, buildings renovation is a subject of special interest since the building and construction sector is the main body responsible for energy consumption and emissions. Hence, it is necessary to concentrate on refurbishment to achieve Europe’s climate neutrality by 2050 according to European Agenda goals. The BIM4EEB Project, a BIM-based fast toolkit for the efficient renovation of residential buildings, directs the attention toward developing an exhaustive toolkit based on Building Information Modeling (BIM) to be adopted in the renovation of existing residential buildings, to make the flow of information efficient, decreasing intervention working time while improving building performances, quality, and comfort for inhabitants. BIM4EEB is developing a BIM management system connected to an operational and multifunctional toolkit for various architecture, engineering, and construction (AEC) stakeholders, integrating a set of tools for improving BIM adoption in renovation environments based on an interoperable flow of information. This paper presents the Horizon2020 Project and the framework used to develop the toolkit. In addition, the first outcomes of the toolkit development are outlined. The validation procedure in real environments has started to demonstrate the efficacy and applicability of the methodology and tools. Although the project is still in progress, benefits connected to the framework and the BIM-based toolkit result in an enhanced building renovation process.

A novel multifactor algorithm is developed with the aim of estimating GHG emissions in the EU countries and forecasting different future scenarios. This is created starting from (1) GDP, (2) population and (3) renewable energy share (RES). The determination coefficient (R2) of the multiple regression adopted reaches a value of 0.96; thus, only 4% of the GHG variation cannot be explained by the combination of the three variables. Germany is removed from the model after analysing the statistical outliers, as it presents an unusual behaviour within the European context. Also, France, Italy and Ireland are removed in the forecast analysis since they are characterised by corrected weighting values above the threshold value of the algorithm (0.156). The results show that GHG emissions decrease 14% in a low-growth-rate scenario, increase 24% in an average-growth scenario and increase 104% in a high-growth-rate scenario. Countries that improve the most are the ones that are currently underdeveloped in RES and are expected to decrease their population in the future (Croatia, Latvia, Cyprus and Greece). Other countries currently well positioned but with expected population growth (Sweden, Luxemburg and Denmark) or with expected intense GDP growth (Estonia and Malta) may lack decarbonisation levers. Therefore, policy makers should introduce additional subsidy schemes and tax exemptions in both developed and less developed countries to meet EU decarbonisation targets.

The decarbonization of the built environment, both in new construction and renovation, is crucial to mitigate its relevant impact on climate change and achieve the Paris Agreement goals. This study presents a systematic LCA-based methodology to assess the whole-life carbon emissions of buildings, applied to a proposal for the regeneration of one of Milan, Italy’s, disused railway yards. As an entry for the 2020 Reinventing Cities competition, Scalo Lambrate is a project for a mainly residential neighborhood with a public park. Strategies to reduce carbon emissions deriving both from the operational energy and construction and maintenance were evaluated and their effects compared to a reference scenario over a time horizon of 100 years. The results show that, while the opportunities to reduce carbon emissions during the use phase are somehow limited due to the already stringent performance requirements for new builds, the use of fast-growing biogenic materials for construction materials, even if mixed with more traditional ones, can provide a significant reduction in the global warming potential over the whole life cycle, with a reduction of 70% compared to the baseline. The remaining emissions can be offset with afforestation initiatives, which, however, must be assessed against land use issues.

This paper proposes design guidelines to enhance energy efficiency and energy generation potential in active solar buildings. Additionally, it presents a variety of optimized urban forms characterized by attributes such as shape, layout, and number of buildings on the plot. These urban configurations are classified into solar species, each associated with a distinct range of high passive and active solar potential. These results were achieved by developing and applying a simulation-driven, multi-objective optimization technique for the early-stage design of a residential building cluster in a temperate climate. This method leverages both passive and active energy indicators, employing a genetic algorithm to identify optimal forms that maximize active solar potential while also minimizing operational energy demand. The approach utilizes a parametric modelling routine that relies on vertical cores and horizontal connections to produce design iterations featuring irregular geometry, while ensuring structural continuity and means of egress. The findings reveal a significant variability in onsite energy generation, with optimized solutions differing by a factor of 2.5 solely based on shape, underscoring the critical role of active solar potential. Taken together, these results hint at the descriptive and predictive capabilities of these solar species, making them a promising heuristic model for characterizing urban form in relation to energy performance.

Efficient use of energy in the construction sector is a pillar of the European Union’s 2050 climate protection goals, yet legislation makes no explicit reference to urban morphology or building form, which are recognized as key to energy performance in buildings. Rapidly changing energy standards and new requirements for on-site energy production demand a vigorous scrutiny of established urban typologies that are largely the product of an older energy regime. The research explores a set of 312 building shapes with floor-to-area ratio (FAR) of 3 within a given plot to identify emerging trends, ranges, and correlations between geometric variables, visual comfort, and energy indicators. Cases are grouped and evaluated in relation to three main urban typologies to highlight unique features related to each typology. The paper also compares two groups of results related to passive and active solar potential, respectively, to identify formal traits that are specific to each of these two design strategies. Finally, the research ranks design options based on total energy use taking into account the energy need for artificial lighting as well as contributions from both passive energy savings and active energy production. Results show that energy demand across cases varies by a factor 2 for passive strategies and a factor 5 when active potential is considered based on shape alone. Best results are clearly positioned at the two extremes of the geometric and proportional range. On the one hand, low-rise compact bar and courtyard buildings that are perhaps most prevalent in our cities today may be effectively retrofitted to meet active energy targets. On the other hand, extremely tall and slim towers appear to be the only typology in the study with the potential to achieve zero-energy status by virtue of their form alone. The work sheds light on the formal implications of EU energy mandates and offers a glimpse of how buildings may adapt to the combined selective pressures of high on-site energy fraction and low energy use to shape our future cities.

Structural Health Monitoring (SHM) of existing bridges increasingly relies on dynamic measurements to assess structural performance and detect potential damage. However, the practical implementation of long-term vibration-based monitoring is still constrained by the volume of data required and the complexity of continuous acquisition systems. In the context of ensuring the safety and performance of existing bridge infrastructure, vibration-based monitoring offers a powerful tool for detecting changes in structural behavior. This study presents an extended investigation of dynamic monitoring applied to composite steel–concrete viaducts, focusing particularly on the signal-analysis framework and methodological enhancements. Short-duration accelerometric records are processed through an automated signal-selection pipeline and advanced modal-parameter extraction algorithms to yield identification of modal features. Emphasis is placed on the statistical evaluation of modal-parameter stability, effects of operational and environmental variability, and the potential for long-term trend detection. The results highlight the limits of short-length recordings when OMA techniques are applied. Nevertheless, appropriate signal processing and data handling can provide acceptable insights into the dynamic characteristics of large bridge systems. The methodological findings provide a foundation for improved monitoring workflows, showing the amount of information that can be retrieved using a cost-effective hardware deployment and supporting further development toward structural digital twins.