Communication framework to support more effective onsite construction monitoring

The UK construction industry has recently witnessed an increasing demand for cost-reduction strategies due to the strict government regulations on BIM implementation. This adoption will certainly lead to a continuous work improvement, better project delivery and communication. Although the UK government has set a target of 15–20% saving on the costs of capital projects by the full implementation of BIM level 2 in 2016, this figure is unlikely to be met since the majority of construction companies are still spending approximately £20 billion per year on rebuilding and repairing the construction defects caused by miscommunication. This research addresses the problem of communication using traditional methods (i.e. communication through paper-based documents and drawings) and its impact during the construction phase in relation to clash detection. Next, we will present a communication framework using advanced visualisation technique such as augmen ted reality (AR) combined with a BIM model with an easy access to the IFC f ile on site for a compliance checking between the BIM model and the actual co nstruction site. Subsequently, site inspection can be performed more efficiently, and with more reliability. Furthermore, early warning on future occu rring clashes can be given. To reach our objectives, the research has been designed using real case scenario, following two phases of implementation. The first phase include the communication study and consists of determining users requiring a ssistance with regard to site monitoring and inspection, whereas the second, built on the results of the first phase to specify and implement the mobile AR syste

Framing digital tools and techniques in built heritage 3D modelling: the problem of level of detail in a simplified environment

Recently, the built heritage sector has witnessed an increase demand for 3D models of historical sites mainly due to the widespread of new technologies in buildings’ surveying. Although these technologies have been credited for enabling highly detailed 3D modelling of the built heritage, their implementation is still so complex and costly. This research aims to explore the possibility of implementing new low-cost digital acquisition technologies and modelling techniques as an alternative to the existing expensive ones in terms of level of detail (LOD), as an attempt to enable low-skilled users in simplified environment, which are faced paced leaning milieus in education, places with high constraints, or developing countries, to practically learn about their built heritage; consequently, contribute to its preservation. To achieve this purpose, the most diffused SFM and laser scanning open-source packages were first cross-compared using web-content analysis data collection method. Afterwards, the best programme from each category namely; Autodesk 123D catch and Reconstructme, accompanied with Canon D550 camera and Xbox Kinect, respectively, were intensively evaluated through an experiment. The analysis of the findings has suggested that low-cost close-range photogrammetry can replace laser scanning when there is a lack of funding and time

Impact of household transitions on domestic energy consumption and its applicability to urban energy planning

The household sector consumes roughly 30% of Earth’s energy resources and emits approximately 17% of its carbon dioxide. As such, developing appropriate policies to reduce the CO2 emissions, which are associated with the world’s rapidly growing urban population, is a high priority. This, in turn, will enable the creation of cities that respect the natural environment and the well-being of future generations. However, most of the existing expertise focuses on enhancing the thermal quality of buildings through building physics while few studies address the social and behavioral aspects. In fact, focusing on these aspects should be more prominent, as they cause between 4% and 30% of variation in domestic energy consumption. Premised on that, the aim of this study was to investigate the effect in the context of the UK of household transitions on household energy consumption patterns. To achieve this, we applied statistical procedures (e.g., logistic regression) to official panel survey data comprising more than 5500 households in the UK tracked annually over the course of 18 years. This helped in predicting future transition patterns for different household types for the next 10 to 15 years. Furthermore, it enabled us to study the relationship between the predicted patterns and the household energy usage for both gas and electricity. The findings indicate that the life cycle transitions of a household significantly influence its domestic energy usage. However, this effect is mostly positive in direction and weak in magnitude. Finally, we present our developed urban energy model “EvoEnergy” to demonstrate the importance of incorporating such a concept in energy forecasting for effective sustainable energy decision-making

A smart urban energy prediction system to support energy planning in the residential sector

The UK residential sector accounts for approximately 27%., and 17% of the country energy consumption and its CO2 emission, respectively. Thus, developing appropriate polices to reduce the environmental factors, which are associated with the CO2 emissions of a rapidly growing urban population, constitutes a high priority. Moreover, ensures the creation of cities that respect the natural environment and the well-being of future generations. While a great deal of expertise on detailing and constructing low-energy buildings and cities has been developed, it is fragmented and does not consider the concept of household life-cycle demographic transitions in the prediction of residential energy consumption. This research aimed to develop an integrated 3D urban energy prediction tool which supports decision-making for a sustainable energy monitoring and planning in the residential sector. This, while considering the CK household demographic transition patterns in the energy prediction process. To attain the above aim, the research embraced a mixed-methods methodology with 4 stages of practical implementations. In stages 1 and 2, statistical procedures such as binary logistic regression, were applied to the British household panel data survey (BHPS) to attain the two following objectives. First, to analyse the socio-economic and demographic factors affecting the UK household transitions; consequently, predict future transition patterns in the next 10-15 years. Secondly, to investigate the impact of the predicted transition patterns on the residential energy consumption. The examination of the findings indicated that the nature of independent factors and their degree of influence on household transition patterns were not consistent across the 10-15 years. Moreover, it advised that household transitions mostly have a positive but weak effect on their energy usage. Based on those findings, a linear regression model was developed to predict the households' future electricity usage in function of their transition, demographic and socio­ economic variables. In phases 3 and 4, a 3D urban energy prediction tool (EvoEnergy) was developed by first building a 3D semantic model of a pilot area in Nottingham city. Moreover, by integrating the research findings from stages 1 and 2 into EvoEnergy using computer scripting, open-source game technology, and 3D visualisation techniques. Finally, despite the facts that the benchmarking of EvoEnergy highlighted some areas for improvement, it has advised that EvoEnergy has the ability to predict domestic electricity consumption at the building and neighbourhood levels with a good accuracy(+/- 5% error)

Big Data to support sustainable urban energy planning: the EvoEnergy project

Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socioeconomic to meet the strict CO2 emission targets. This paper builds upon our previous work on the effect of household transition on residential energy consumption where we developed a 3D urban energy prediction system (EvoEnergy) using the old UK panel data survey namely the British household panel data survey (BHPS). In particular, the aim of the present study is to examine the validity and reliability of EvoEnergy under the new UK household longitudinal study (UKHLS) launched in 2009. To achieve this aim, the household transition and energy prediction modules of EvoEnergy have been tested under both datasets using various statistical techniques such as Chow test. The analysis of the results advised that EvoEnergy remains a reliable prediction system and had a good prediction accuracy (MAPE ≃ 5%) when compared to actual EPC data. From this premise, we recommend researchers, who are working on data-driven energy consumption forecasting to consider merging the BHPS and UKHLS datasets. This will, in turn, enable them to capture the bigger picture of different energy phenomena such as fuel poverty; consequently, anticipate problems with policy prior to their occurrence. Finally, the paper concludes by discussing two scenarios of EvoEnergy development in relation to energy policy and decision-making

Construction site communication study using the RAM management system for BIM adaptation

The UK’s construction industry is witnessing an annual increase in costs due to a lack of communication between the different organizational operators on the construction site that often leads to construction defects. Meanwhile, a cost-reduction strategy plan using BIM has become a fundamental requirement for the government, aiming to keep costs under control. To facilitate BIM adoption in the industry, the BIM strategy was introduced in four phases, with each stage entailing a number of criteria. The industry has seen a global reaction to the Level 2 BIM program and a significant cost saving of 840M in 2013/14 in Europe. However, the industry is unable to match the level 3 BIM, where a collaborative model file server is required as a common sharable platform to achieve efficient communication. This study contributes toward formulating a communication framework in the UK industry to understand communication issues and manage defects. A survey was targeted at construction industry practitioners and academics, with a total number of 328 participants

Visualisation in energy eco-feedback systems: a systematic review of good practice

While adopting eco-feedback systems can lead to significant energy savings, in the region of 5–20%, research has shown that the inappropriate use of visualisation patterns and techniques decreases their effectiveness. However, existing reviews on energy feedback visualisation provide little guidance on when to use them and how to enhance their effectiveness in various scenarios. The uncertainty and lack of clarity surrounding eco-feedback visualisation techniques and their impact on end-userengagement present challenges to the design of ecofeedback systems. This paper presents the first systematic review of a wide range of energy eco-feedback visualisation techniques, including, for the first time, Augmented-Reality (AR) and thermal imaging visualisation. We analysed 82 relevant studies published between 2000 and 2021 using the PRISMA protocol for systematic reviews. The visualisation techniques have been reported under five distinct categories, which we have identified: (a) statistical visualisation, (b) architectural and geospatial visualisation, (c) game-based visualisation, (d) artistic visualisation, (e) emerging visualisation. Furthermore, they have been analysed based on the following criteria: type of visualised information, the purpose of use, end-user perception, scholar suggestions, and potential impact. The findings show that statistical visualisation techniques are essential in any energy eco-feedback system. Furthermore, they suggested combining different visualisation techniques to accommodate different user profiles, but such combinations must be carefully planned based on usage scenarios. Following this analysis, a series of considerations and good practice guidelines are presented for each of the reviewed techniques to assist practitioners in this area (e.g., designers and researchers) while providing recommendations for future work

From discovering to delivering: a critical reflection on eco-feedback, application design, and participatory research in the United Kingdom

The area of Eco-Feedback has received significant attention in recent years. Whilst there have been increasing calls to move ‘beyond feedback’ and consider the wider social, organisational and cultural context that feedback sits within, the involvement of community members in the design process of eco-feedback applications, known as co-design, has been limited. This study addresses that research gap through working collaboratively with community members to develop an accessible eco-feedback interface. First, we conducted an online survey questionnaire with 151 respondents with distinct socio-economic characteristics and environmental knowledge to get insights into their preferences about different aspects of the eco-feedback interface. Secondly, based on the survey findings, 20 community members living in Nottingham, UK, worked collaboratively to develop interface design proposals. Finally, the design of the eco-feedback interface was finalised based on the community interface prototypes and suggestions. The developed interface contains multiple information panels with options for expanding to gain deeper levels of information and a community space allowing for community interactions and sharing of information and actions. This research sheds new light on the challenges of utilising co-design principles to build eco-feedback interfaces. Specifically, we highlight the potential for interactions between community members during the design stages to allow for the generation of innovative ideas (e.g. Integration of third-party applications) moving the interface beyond feedback leading to greater adoption and energy savings