Promoting energy efficiency in the built environment through adapted BIM training and education

The development of new climate change policies has increased the motivation to reduce energy use in buildings, as reflected by a stringent regulatory landscape. The construction industry is expected to adopt new methods and strategies to address such requirements, focusing primarily on reducing energy demand, improving process efficiency and reducing carbon emissions. However, the realisation of these emerging requirements has been constrained by the highly fragmented nature of the industry, which is often portrayed as involving a culture of adversarial relationships and risk avoidance, which is exacerbated by a linear workflow. Recurring problems include low process efficiency, delays and construction waste. Building information modelling (BIM) provides a unique opportunity to enhance building energy efficiency (EE) and to open new pathways towards a more digitalised industry and society. BIM has the potential to reduce (a) waste and carbon emissions, (b) the endemic performance gap, (c) in-use energy and (d) the total lifecycle impact. BIM also targets to improve the whole supply chain related to the design, construction as well as the management and use of the facility. However, the construction workforce is required to upgrade their skills and competencies to satisfy new requirements for delivering BIM for EE. Currently, there is a real gap between the industry expectations for employees and current training and educational programmes. There is also a set of new requirements and expectations that the construction industry needs to identify and address in order to deliver more informed BIM for EE practices. This paper provides an in-depth analysis and gap identification pertaining to the skills and competencies involved in BIM training for EE. Consultations and interviews have been used as a method to collect requirements, and a portfolio of use cases have been created and analysed to better understand existing BIM practices and to determine current limitations and gaps in BIM training. The results show that BIM can contribute to the digitalisation of the construction industry in Europe with adapted BIM training and educational programmes to deliver more informed and adapted energy strategies

Towards a semantic Construction Digital Twin: directions for future research

As the Architecture, Engineering and Construction sector is embracing the digital age, the processes involved in the design, construction and operation of built assets are more and more influenced by technologies dealing with value-added monitoring of data from sensor networks, management of this data in secure and resilient storage systems underpinned by semantic models, as well as the simulation and optimisation of engineering systems. Aside from enhancing the efficiency of the value chain, such information-intensive models and associated technologies play a decisive role in minimising the lifecycle impacts of our buildings. While Building Information Modelling provides procedures, technologies and data schemas enabling a standardised semantic representation of building components and systems, the concept of a Digital Twin conveys a more holistic socio-technical and process-oriented characterisation of the complex artefacts involved by leveraging the synchronicity of the cyber-physical bi-directional data flows. Moreover, BIM lacks semantic completeness in areas such as control systems, including sensor networks, social systems, and urban artefacts beyond the scope of buildings, thus requiring a holistic, scalable semantic approach that factors in dynamic data at different levels. The paper reviews the multi-faceted applications of BIM during the construction stage and highlights limits and requirements, paving the way to the concept of a Construction Digital Twin. A definition of such a concept is then given, described in terms of underpinning research themes, while elaborating on areas for future research

Optimizing energy efficiency in operating built environment assets through building Information modeling: a case study

Reducing carbon emissions and addressing environmental policies in the construction domain has been intensively explored with solutions ranging from energy efficiency techniques with building informatics to user behavior modelling and monitoring. Such strategies have managed to improve current practices in managing buildings, however decarbonizing the built environment and reducing the energy performance gap remains a complex undertaking that requires more comprehensive and sustainable solutions. In this context, building information modelling (BIM), can help the sustainability agenda as the digitalization of product and process information provides a unique opportunity to optimize energy-efficiency-related decisions across the entire lifecycle and supply chain. BIM is foreseen as a means to waste and emissions reduction, performance gap minimization, in-use energy enhancements, and total lifecycle assessment. It also targets the whole supply chain related to design, construction, as well as management and use of facilities, at the different qualifications levels (including blue-collar workers). In this paper, we present how building information modelling can be utilized to address energy efficiency in buildings in the operation phase, greatly contributing to achieving carbon emissions targets. In this paper, we provide two main contributions: (i) we present a BIM-oriented methodology for supporting building energy optimization, based on which we identify few training directions with regards to BIM, and (ii) we provide an application use case as identified in the European research project “Sporte2” to demonstrate the advantages of BIM in energy efficiency with respect to several energy metrics

An intelligent semantic system for real-time demand response management of a thermal grid

“Demand Response” energy management of thermal grids requires consideration of a wide range of factors at building and district level, supported by continuously calibrated simulation models that reflect real operation conditions. Moreover, cross-domain data interoperability between concepts used by the numerous hardware and software is essential, in terms of Terminology, Metadata, Meaning and Logic. This paper leverages domain ontology to map and align the semantic resources that underpin building and district energy management, with a focus on the optimization of a thermal grid informed by real-time energy demand. The intelligence of the system is derived from simulation-based optimization, informed by calibrated thermal models that predict the network’s energy demand to inform (near) real-time generation. The paper demonstrates that the use of semantics helps alleviate the endemic energy performance gap, as validated in a real district heating network where 36% reduction on operation cost and 43% reduction on CO2 emission were observed compared to baseline operational data

Medial Frontal White and Gray Matter Contributions to General Intelligence

The medial orbitofrontal cortex (mOFC) and rostral anterior cingulate cortex (rACC) are part of a wider neural network that plays an important role in general intelligence and executive function. We used structural brain imaging to quantify magnetic resonance gray matter volume and diffusion tensor white matter integrity of the mOFC-rACC network in 26 healthy participants who also completed neuropsychological tests of intellectual abilities and executive function. Stochastic tractography, the most effective Diffusion Tensor Imaging method for examining white matter connections between adjacent gray matter regions, was employed to assess the integrity of mOFC-rACC pathways. Fractional anisotropy (FA), which reflects the integrity of white matter connections, was calculated. Results indicated that higher intelligence correlated with greater gray matter volumes for both mOFC and rACC, as well as with increased FA for left posterior mOFC-rACC connectivity. Hierarchical regression analyses revealed that DTI-derived FA of left posterior mOFC-rACC uniquely accounted for 29%–34% of the variance in IQ, in comparison to 11%–16% uniquely explained by gray matter volume of the left rACC. Together, left rACC gray matter volume and white matter connectivity between left posterior mOFC and rACC accounted for up to 50% of the variance in general intelligence. This study is to our knowledge the first to examine white matter connectivity between OFC and ACC, two gray matter regions of interests that are very close in physical proximity, and underscores the important independent contributions of variations in rACC gray matter volume and mOFC-rACC white matter connectivity to individual differences in general intelligence

A prospective study of physician-observed concussion during a varsity university hockey season: White matter integrity in ice hockey players. Part 3 of 4

Object: The aim of this study was to investigate the effect of repetitive head impacts on white matter integrity that were sustained during 1 Canadian Interuniversity Sports (CIS) ice hockey season, using advanced diffusion tensor imaging (DTI). Methods: Twenty-five male ice hockey players between 20 and 26 years of age (mean age 22.24 ± 1.59 years) participated in this study. Participants underwent pre- and postseason 3-T MRI, including DTI. Group analyses were performed using paired-group tract-based spatial statistics to test for differences between preseason and postseason changes. Results: Tract-based spatial statistics revealed an increase in trace, radial diffusivity (RD), and axial diffusivity (AD) over the course of 1 season. Compared with preseason data, postseason images showed higher trace, AD, and RD values in the right precentral region, the right corona radiata, and the anterior and posterior limb of the internal capsule. These regions involve parts of the corticospinal tract, the corpus callosum, and the superior longitudinal fasciculus. No significant differences were observed between preseason and postseason for fractional anisotropy. Conclusions: Diffusion tensor imaging revealed changes in white matter diffusivity in male ice hockey players over the course of 1 season. The origin of these findings needs to be elucidated