Author
M. Lurdes Simões
Other affiliations: Faculdade de Engenharia da Universidade do Porto
Bio: M. Lurdes Simões is an academic researcher from University of Porto. The author has contributed to research in topics: Thermal comfort & Building information modeling. The author has an hindex of 11, co-authored 19 publications receiving 272 citations. Previous affiliations of M. Lurdes Simões include Faculdade de Engenharia da Universidade do Porto.
Papers
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TL;DR: The result is a thorough review of the work done by other authors in relevant fields, comprising the entire spectrum from on-site data acquisition, through the generation of Building Energy Models (BEM), data transfer to energy analysis software and, finally, the identification of major issues throughout this process.
Abstract: Building Information Modeling (BIM), as a rising technology in the Architecture, Engineering and Construction (AEC) industry, has been applied to various research topics from project planning, structural design, facility management, among others. Furthermore, with the increasing demand for energy efficiency, the AEC industry requires an expeditious energy retrofit of the existing building stock to successfully achieve the 2020 Energy Strategy targets. As such, this article seeks to survey the recent developments in the energy efficiency of buildings, combining energy retrofitting and the technological capabilities of BIM, providing a critical exposition in both engineering and energy domains. The result is a thorough review of the work done by other authors in relevant fields, comprising the entire spectrum from on-site data acquisition, through the generation of Building Energy Models (BEM), data transfer to energy analysis software and, finally, the identification of major issues throughout this process. Additionally, a BIM-based methodology centered on the acquired knowledge is presented. Solutions for as-built data acquisition such as laser scanning and infrared thermography, and on-site energy tests that benefit the acquisition of energy-related data are explored. The most predominant BIM software regarding not only energy analysis but also model development is examined. In addition, interoperability restrictions between BIM and energy analysis software are addressed using the Industry Foundation Classes (IFC) and Green Building Extensible Markup Language (gbXML) schemes. Lastly, the article argues the future innovations in this subject, predicting future trends and challenges for the industry.
115 citations
TL;DR: A full-fledged laser scanning framework for geometric data acquisition, comprising the entire spectrum from planning, surveying and data analysis is introduced, that details the necessary steps to acquire a point cloud that is applicable to BIM modelling.
Abstract: Laser scanning, as a rising topic within the Architecture, Engineering and Construction (AEC) industry, has been increasing both in importance and practice as a means of gathering in-situ geometric data. Several studies have covered possible applications of this technology, from construction monitoring to damage assessment, with Building Information Modelling (BIM) being one of its focus. Despite this, to present, no research was found to fully explore the laser scanning survey process, with most studies either focusing the process after the point cloud acquisition or after its conversion to BIM. To help fill this knowledge gap, the present article introduces a full-fledged laser scanning framework for geometric data acquisition, comprising the entire spectrum from planning, surveying and data analysis. The result is a framework that details the necessary steps to acquire a point cloud that is applicable to BIM modelling. The framework is validated through its application to a recently renewed bus station in Porto, Portugal. Relevant conclusions regarding setting selection, station positioning, optimization, point cloud decimation and treatment, required resolution, along other topics, are drawn through laboratory tests and the previously mentioned case study.
50 citations
TL;DR: The concept of a stochastic material database for probabilistic building performance simulation was developed and illustrated, and the source of uncertainty in the material data was addressed and uncertainty in different data levels was analyzed.
21 citations
TL;DR: It could be concluded that the PMV-PPD and aPMV models overestimated the cooling sensation and an alternative approach based on the correlation between SET* and dissatisfied voters established through the thermal preference method provided a wider comfort range that appears to be adequate.
20 citations
TL;DR: An occupied dwelling in Porto, Portugal, was investigated and a novel methodology for the detection of occupant actions through indoor environmental data was proposed, achieving an accuracy of more than 99.7% of all the intended actions to be detected.
20 citations
Cited by
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TL;DR: In this article, the authors proposed the use of photovoltaic (PV) technologies to add extra functionalities in a building by replacing the conventional structural material and harnessing benign electricity aesthetically from PV.
133 citations
TL;DR: This study collects all the available techniques for the U-value assessment, explaining their weak and strength points, together with analogies and differences among the literature experiences, and focuses on the quantitative infrared thermography (IRT).
107 citations
TL;DR: This research helps establish the state-of-the-art of DT in the civil engineering sector and suggests future DT development by extracting DT research clusters based on the co-occurrence analysis of paper keywords' and the relevant DT constituents.
107 citations
TL;DR: This is the first paper to present a review of this act's implementation over the past decade, and it's expected that the efforts as constituting significant guidance for evaluating the EEP in the Chinese civil building sector will be treated as an example for other developing countries to evaluate and revise their BEE acts.
107 citations
TL;DR: It is demonstrated that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling and the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.
Abstract: Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.
105 citations