A receptive-responsive tool for customizing occupant's thermal comfort and maximizing energy efficiency by blending BIM data with real-time information

Birgönül, Zeynep

doi:10.1108/sasbe-11-2020-0175

Cited by 21 publications

(13 citation statements)

References 61 publications

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“…The proposed framework can simultaneously minimize building energy consumption, life cycle costs, and environmental impacts. Similar research has been conducted by [14][15][16].…”

Section: Introductionsupporting

confidence: 52%

Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework

et al. 2022

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Assessing the energy performance of existing residential buildings (ERB) has been identified as key to improving building energy efficiency and reducing associated greenhouse gas emissions in Canada. However, identifying optimal retrofit packages requires a significant amount of knowledge of building energy modelling, and it is a time-consuming and laborious process. This paper proposed a data-driven framework that combines machine learning, multi-objective optimization, and multi-criteria decision-making techniques to evaluate the energy performance of ERB and thereby formulate optimal retrofit plans. First, an artificial neural network (ANN) was developed to predict the energy performance of a wide range of retrofit packages. A genetic algorithm was employed to determine the best structure and hyperparameters of the ANN model. Then, the energy consumption results were integrated with environmental and economic impact data to evaluate the environmental and economic performance of retrofit packages and thereby identify Pareto optimal solutions. Finally, a multi-criteria decision-making method was used to select the best retrofit packages among the optimal solutions. The proposed framework was validated using data on a typical residential building in British Columbia, Canada. The results indicated that this framework could effectively predict building energy performance and help decision-makers to make an optimal decision when choosing retrofit packages.

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“…The proposed framework can simultaneously minimize building energy consumption, life cycle costs, and environmental impacts. Similar research has been conducted by [14][15][16].…”

Section: Introductionsupporting

confidence: 52%

Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework

et al. 2022

View full text Add to dashboard Cite

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“…Therefore, the user both controls and manages the context. [38]. Sensor technologies are capable of capturing almost all kinds of data, but without the interaction of the smart user, the data would just perish and I don't see those types of smart systems could be considered as innovative anymore.…”

Section: Current Design and Production Practices In Aec Industrymentioning

confidence: 99%

The adoption of multidimensional exploration methodology to the design-driven innovation and production practices in AEC industry

Birgönül¹,

Carrasco²

2021

JCEMI

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The new horizon for research & development in AEC is directed through an interdisciplinary design-thinking approach. The design paradigms are not only as they were about form and function but now also about efficiency, sustainability, productivity, desirability, feasibility, and viability in current day. As a result of this approach, the term design innovation approves oneself. Correspondingly, emerging technologies such as robotic fabrication & construction tools, self-fabrication & assembly, information and communication technologies, computer science and related software technologies, enable the designers and engineers to push the edges of their creativity, productivity and sensitivity upon global issues. The emergence of those new technologies is defined as digitalism, and both academy and industry have almost totally adapted to this digital-design thinking approach. Consequently, the smooth communication between machines, makers and designers dominates the progression in architecture, science, technology and engineering. However, because of this shift, a research gap has emerged between new academic research and classical industrial production as theory and practice do not overlap anymore. For the adoption of this emergent holistic design-thinking approaches; a new learning methodology has been introduced by the authors under the title of 'Multidimensional Exploration Methodology.

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“…Moreover, depending on the purpose of the inspection, the thermographic analysis can be performed either qualitatively or quantitatively (Janků et al , 2017; Birgonul, 2021). Qualitative IRT is applied when the focus is on the relative pixel values or the temperature distribution, and the exact measurement of temperature in the thermal images is not needed.…”

Section: Introductionmentioning

confidence: 99%

The development of a digitally enhanced visual inspection framework for masonry bridges in the UK

Talebi

Al-Adhami

et al. 2022

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Purpose The utilisation of emerging technologies for the inspection of bridges has remarkably increased. In particular, non-destructive testing (NDT) technologies are deemed a potential alternative for costly, labour-intensive, subjective and unsafe conventional bridge inspection regimes. This paper aims to develop a framework to overcome conventional inspection regimes' limitations by deploying multiple NDT technologies to carry out digital visual inspections of masonry railway bridges. Design/methodology/approach This research adopts an exploratory case study approach, and the empirical data is collected through exploratory workshops, interviews and document reviews. The framework is implemented and refined in five masonry bridges as part of the UK railway infrastructure. Four NDT technologies, namely, terrestrial laser scanner, infrared thermography, 360-degree imaging and unmanned aerial vehicles, are used in this study. Findings A digitally enhanced visual inspection framework is developed by using complementary optical methods. Compared to the conventional inspection regimes, the new approach requires fewer subjective interpretations due to the additional qualitative and quantitative analysis. Also, it is safer and needs fewer operators on site, as the actual inspection can be carried out remotely. Originality/value This research is a step towards digitalising the inspection of bridges, and it is of particular interest to transport agencies and bridge inspectors and can potentially result in revolutionising the bridge inspection regimes and guidelines.

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A receptive-responsive tool for customizing occupant's thermal comfort and maximizing energy efficiency by blending BIM data with real-time information

Cited by 21 publications

References 61 publications

Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework

Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework

The adoption of multidimensional exploration methodology to the design-driven innovation and production practices in AEC industry

The development of a digitally enhanced visual inspection framework for masonry bridges in the UK

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