Offsite construction is gaining attention due to government policies promoting automation and productivity. Therefore, understanding the impact of building design on construction cost and the carbon footprint associated with offsite construction is important for the improved sustainability and climate resilience of the built environment. This study aims to develop a system approach, with the aid of building information modeling (BIM), for 3D geometric modeling and automatic generative design toward optimizing the carbon footprint and construction cost from offsite construction. A mathematical formulation is proposed to represent the topological relationships between different kinds of precast and cast-in-situ elements, which in turn, underpin 3D geometric modeling for possible geometric variations within precast buildings. New generative algorithms are developed to automatically manipulate building geometrics subject to pre-defined constraints, and create parametric BIM models in compliance with material types assigned by users. A BIM-based automation tool is developed to extract and match the model geometry/material information with a customized BIM object library, through which carbon emission factors and cost coefficients can be retrieved for multi-criteria sustainability analysis. The proposed new approach empowers 3D geometric modeling and geometry-based design automation, which enable comprehensive exploration of design alternates in precast construction. The proposed new method is illustrated via a case study that investigates the impact of different design variations on embodied carbon and construction cost of precast structures. The proposed BIM development takes around 30 minutes to create 1,000–1,500 new design options. Besides, it produces design options that contain a 30% less carbon footprint and construction cost than reference buildings from the literature. The results indicate that our proposed method can support automated design exploration at early stages, which help to identify optimal solutions for more informed decision making.

Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework based on building information modelling (BIM) and machine learning data-driven models is presented to analyze the optimum thermal comfort for indoor environments with the effect of natural ventilation. BIM provides geometrical and semantic information of the built environment, which are leveraged for setting the computational domain and boundary conditions of computational fluid dynamics (CFD) simulation. CFD modelling is conducted to obtain the flow field and temperature distribution, the results of which determine the thermal comfort index in a ventilated environment. BIM–CFD provides spatial data, boundary conditions, indoor environmental parameters, and the thermal comfort index for machine learning to construct robust data-driven models to empower the predictive analysis. In the neural network, the adjacency matrix in the field of graph theory is used to represent the spatial features (such as zone adjacency and connectivity) and incorporate the potential impact of interzonal airflow in thermal comfort analysis. The results of a case study indicate that utilizing natural ventilation can save cooling power consumption, but it may not be sufficient to fulfil all the thermal comfort criteria. The performance of natural ventilation at different seasons should be considered to identify the period when both air conditioning energy use and indoor thermal comfort are achieved. With the proposed new framework, thermal comfort prediction can be examined more efficiently to study different design options, operating scenarios, and changeover strategies between various ventilation modes, such as better spatial HVAC system designs, specific room-based real-time HVAC control, and other potential applications to maximize indoor thermal comfort.

Modular building is becoming a common sight due to government policies promoting greater automation and productivity. When moving towards modularity, indoor comfort within volumetric modules, such as levels of humidity and temperature, natural ventilation, and air pollutant transport, have a major effect on human health and well-being. Computational fluid dynamics simulations (CFD) are used to evaluate the efficiency of natural ventilation. However, designers usually find it difficult to visualize the CFD simulation results, which can deepen users’ understanding of the wind environment and help optimize the design of modular buildings. To overcome this challenge, this paper presents an integrated approach based on building information modeling (BIM) and virtual reality (VR), with the aim of analyzing the aerodynamic design and wind comfort for modular buildings. The framework consists of four salient components. First, a new method, combining OpenStreetMap and Dynamo, is proposed to achieve rapid urban modeling of modular buildings. The second step involves the use of CFD to simulate the outdoor wind environment surrounding modular buildings. The third step emphasizes the integration of CFD-computed data with VR applications to create an immersive virtual environment for designers to analyze the wind environment of design alternates. Finally, the visual experience of non-professional users is used to improve the ventilation of the building and support more informed decision marking at the early stage of building design. The proposed framework is illustrated via a case study that focuses on a group of modular housings in the urban area of Singapore. The results indicate that visualization of CFD simulations in VR provides designers with more details regarding the actual space, and it is expected to help optimize the architectural design.

Residential building construction is resource-intensive and significantly impacts the environment by embodied and operational carbon emissions. This study has adopted a parametric building information modelling (BIM)-based approach for a residential building to analyse its lifecycle carbon performance and to evaluate the optimisation potential through alternative material use and design. The study looks at a residential development project, applying an automatic calculation and analysis tool of upfront embodied carbon and BIM-based lifecycle energy simulation to predict carbon emissions from operating the built spaces. A parametric BIM model has been established to aid energy simulation and operational carbon assessment across a 50-year building lifetime, considering 1.5 °C Net-Zero World and 3 °C Hot House World climate scenarios. Various improvement opportunities for future residential development projects, from material selection to operational efficiencies, are explored. This includes quantitative analysis on architectural-structure design, low-carbon construction materials (e.g., cement substitutes, steel scraps, and green hydrogen steel), and novel design for construction approaches (such as modular integrated construction), with discussion around their impacts on optimising the building lifecycle carbon performance. This study provides a deeper understanding and insights into the lifecycle performance of residential buildings to facilitate further exploration of achieving a more sustainable and low-carbon built environment.

Building Information Modeling (BIM) and sustainable buildings are two future cornerstones of the Architectural, Engineering and Construction (AEC) industry. In Singapore’s context, the Green Mark (GM) scoring system is prevalently used to assess the sustainability index of green buildings. BIM provides the semantic and geometry information of buildings, which is proliferated as the technological and process backbone for the green building assessment. This research, through vast literature reviews, identified that the current procedure of achieving a Green Mark score is tedious and cumbersome, which hampers productivity, especially in the calculation of building envelope thermal performance. Furthermore, the project stakeholders work in silos, in a non-collaborative, manual and 2D-based environment for generating relevant documentation to achieve the requisite green mark score. To this end, a cloud-based BIM platform was developed, with the aim of encouraging project stakeholders to collaboratively generate the project’s green mark score digitally in accordance with the regulatory requirements. Through this research, the authors have validated the Envelope Thermal Transfer Value (ETTV) calculation, which is one of the prerequisite criteria to achieve a Green Mark score, through a case study using the developed cloud-based BIM platform. The results indicated that using the proposed platform enhances the productivity and accuracy as far as ETTV calculation is concerned. This study provides a basis for future research in implementing the proposed platform for other criteria under the Green Mark Scheme.

The increasing number of accidents arising from falling objects from the façade of tall buildings has attracted much attention globally. To regulators, a preventive approach based on a mandatory periodic façade inspection has been deemed as a necessary measure to maintain the functionality and integrity of the façade of tall buildings. Researchers worldwide have been working towards a predictive approach to allow for the assessment of the likely failure during some future period, by measuring the condition of the façade to detect latent defects and anomalies. The methods proposed include laser scanning, image-based sensing and infrared thermography to support the automatic façade visual inspection. This paper aims to review and analyse the state-of-the-art literature on the automated inspection of building façades, with emphasis on the detection and maintenance management of latent defects and anomalies for falling objects from tall buildings. A step-by-step holistic method is leveraged to retrieve the available literature from databases, followed by the analyses of relevant articles in different long-standing research themes. The types and characteristics of façade falling objects, legislations, practices and the effectiveness of various inspection techniques are discussed. Various diagnostic, inspection and analytical methods which support façade inspection and maintenance are analysed with discussion on the potential future research in this field.