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Technological paradigms and digital eras : data-driven visions for building design
The book connects the ICT and the architectural worlds, analyzing modeling, materialization and data-driven visions for design issues at different scales. Furthermore, using sample modeling and materialization tools, it explores the links between performance-driven design approaches and the application of new digital technologies. Intended for architects and urbanists, it provides a theoretical framework to address the implications of the digital revolution in building design and operation. Furthermore, combining insights from IT and ICT with architectural and urban design know-how, it offers engineering professionals a technology-driven interpretation of the building design field.
Book
COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION
Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no façade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models’ façades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we propose a method of combining visibility analysis and neural networks for enriching 3D models with window and door features. In the method, occupancy voxels are fused with classified point clouds, which provides semantics to voxels. Voxels are also used to identify conflicts between laser observations and 3D models. The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate façade openings, which are reconstructed using a 3D model library. Unaffected building semantics is preserved while the updated one is added, thereby upgrading the building model to LoD3. Moreover, Bayesian network results are back-projected onto point clouds to improve points’ classification accuracy. We tested our method on a municipal CityGML LoD2 repository and the open point cloud datasets: TUM-MLS-2016 and TUM-FAÇADE. Validation results revealed that the method improves the accuracy of point cloud semantic segmentation and upgrades buildings with façade elements. The method can be applied to enhance the accuracy of urban simulations and facilitate the development of semantic segmentation algorithms.
Journal Article
Optimal Renovation Strategies for Education Buildings—A Novel BIM–BPM–BEM Framework
The aim of this paper is to propose a novel building information model (BIM)–building performance model (BPM)–building environmental model (BEM) framework to identify the most energy-efficient and cost-effective strategies for the renovation of existing education buildings to achieve the nearly zero-energy goal while minimizing the environmental impact. A case building, the University of Maryland’s Architecture Building, was used to demonstrate the validity of the framework and a set of building performance indicators—including energy performance, environmental impacts, and occupant satisfaction—were used to evaluate renovation strategies. Additionally, this novel framework further demonstrated the interoperability among different digital tools and platforms. Lastly, following a detailed analysis and measurements, the case study results highlighted a particular energy profile as well as the retrofit needs of education buildings. Eight different renovation packages were analyzed with the top-ranking package indicating an energy saving of 62%, carbon emissions reduction of 84%, and long-term cost savings of 53%, albeit with a relatively high initial cost. The most preferable package ranked second in all categories, with a moderate initial cost.
Journal Article
Machine learning and deep learning
Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.
Journal Article
Chinese prototype building models for simulating the energy performance of the nationwide building stock
Building energy modeling (BEM) has become increasingly used in building energy conservation research. Prototype building models are developed to represent the typical urban building characteristics of a specific building type, meteorological conditions, and construction year. This study included four residential buildings and 11 commercial buildings to represent nationwide building types in China. With consideration of five climate zones and different construction years corresponding to national standards, a total of 151 prototype building models were developed. The building envelope properties, occupancy and energy-related behaviors, and heating, ventilation, and air-conditioning (HVAC) system characteristics were defined according to the corresponding building energy efficiency design standards, HVAC design standards, and through other sources, such as questionnaire surveys, on-site measurements, and literature, which reflect the real situation of existing buildings in China. Based on the developed prototype buildings, a large database of 9225 models in 270 cities was further developed to facilitate users to simulate building energy in different cities. In conclusion, the developed prototype building models can represent realistic building characteristics and construction practices of the most common residential and commercial buildings in China, serving as an important foundation for BEM. The models can be used for analyses related to building energy conservation research on typical individual buildings, including energy-saving technologies, advanced controls, and new policies, and providing a reference for the development of building energy codes and standards.
Journal Article
Towards a New Generation of Building Envelope Calibration
Building energy performance (BEP) is an ongoing point of reflection among researchers and practitioners. The importance of buildings as one of the largest activators in climate change mitigation was illustrated recently at the United Nations Framework Convention on Climate Change 21st Conference of the Parties (COP21). Continuous technological improvements make it necessary to revise the methodology for energy calculations in buildings, as has recently happened with the new international standard ISO 52016-1 on Energy Performance of Buildings. In this area, there is a growing need for advanced tools like building energy models (BEMs). BEMs should play an important role in this process, but until now there has no been international consensus on how these models should reconcile the gap between measurement and simulated data in order to make them more reliable and affordable. Our proposal is a new generation of models that reconcile the traditional data-driven (inverse) modelling and law-driven (forward) modelling in a single type that we have called law-data-driven models. This achievement has greatly simplified past methodologies, and is a step forward in the search for a standard in the process of calibrating a building energy model.
Journal Article
3D Variables Requirements for Property Valuation Modeling Based on the Integration of BIM and CIM
The growing rate of urbanization and vertical urban development has aroused the significance of geo-related variables for property units disposed vertically within the same building. Among these, 3D indoor physical and outdoor environmental variables are impacting the property value for each building unit. However, in the literature, the identified 3D variables, by using hedonic pricing models (HPM) for property valuation, are mainly restricted to 3D visualization. Their use in 3D simulation for an accurate evaluation of the property value is still limited. Furthermore, their value is often defined for a specific valuation purpose (e.g., taxation). This paper aims to investigate 3D variables with a significant impact on property value, to combine them with 3D technical requirements and to be integrated in a future valuation model. Moreover, their 3D spatial and non-spatial elements are analyzed to identify which variables can be provided from 3D city models and building scale elements. To accomplish this, the potential of 3D building information modeling (BIM) and city information modeling (CIM) in property valuation is examined. From indoors; BIM/IFC (Industry Foundation Classes) models are the main data sources for structural and living quality variables. While from outdoors, environmental variables and the surrounding building’s information are provided from 3D city models (CityGML).
Journal Article
Using a TAM-TOE model to explore factors of Building Information Modelling (BIM) adoption in the construction industry
Building Information Modelling (BIM) has been adopted as the main technology in the construction industry in many developed countries due to its notable advantages. However, its applications in developing countries are limited. This paper aims to investigate factors which impact on BIM adoption in the construction industry. Twelve external variables were identified by an integrated TAM (Technology Acceptance Model) and TOE (Technology Organization Environment) framework and a systematic review of past studies. A survey was conducted in development, construction, design and consulting companies to investigate the impacts of these 12 external variables on BIM adoption. Using the interval Decision Making Trial and Evaluation Laboratory (DEMATEL) method, retrieved 120 completed questionnaires were analysed. The “Requirements from national policies” was found to be the most significant driving variable of BIM adoption by investigated companies. A further simulation analysis revealed that the “Intention to Use” BIM varied significantly with the change of “Requirements from national policies”, “Standardization of BIM”, and “Popularity of BIM in the industry”. The results lead to the conclusion that government incentives play critical roles in BIM adoption in China. Policy makers could put more efforts into motivation strategies, standardization measures, and BIM culture cultivation to promote BIM applications in the construction industry.
Journal Article
Applications of 3D City Models: State of the Art Review
In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.
Journal Article
A Featureless Approach to 3D Polyhedral Building Modeling from Aerial Images
This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach.
Journal Article