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The reuse of load-bearing building components has the potential to promote the circular economy in the building sector. One recent aspect of the efforts to improve reuse rates in buildings is estimating the reusability of the structural elements. This study develops a probabilistic predictive model using advanced supervised machine learning methods to evaluate the economic reusability of the load-bearing building elements. The results of sensitivity analysis and visualization techniques used in this study reveal that the most important economic factor is the need to purchase reused elements early in a project, which could have cash flow implications. The other most important factors are the potential financial risks, the procurement process, and the labour cost. This study unveils that the relationship between variables is not linear, and none of the identified factors could alone determine if an element is reusable or not. This study concludes that the complex interdependencies of factors affecting reuse cause a high level of uncertainty about the feasibility of reusing the load-bearing building structural components from an economic aspect. Nonetheless, this paper reveals that by using the probability theory foundations and combining it with advanced supervised machine learning methods, it is possible to develop tools that could reliably estimate the economic reusability of these elements based on affecting variables. Therefore, the authors suggest utilizing the approach developed in this research to promote the circularity of materials in different subsectors of the construction industry.

To facilitate the adoption of the circular economy (CE) in the architecture, engineering and construction (AEC) sector, some authors have demonstrated the potential of recent designs that take into account the sustainable management of an asset’s end-of-life (EOL), providing an alternative to the dominant designs that end with demolition. However, there is no review of the literature that encompasses a large range of sustainable designs in the current CE context. This paper provides a critical review of journal papers that deal with the barriers to implementing sustainable designs and approaches to the EOL management of assets that have the potential to fulfil the principles of the CE. Eighteen approaches related to prefabrication, design for change, design for deconstruction, reverse logistics, waste management and closed-loop systems were found. Through an analysis of the barriers that are common among these 18 approaches, we classified them into six different categories (organisational, economical, technical, social, political and environmental). Two Sankey diagrams illustrate the interrelation between the barriers, their categories and the 18 approaches. The diagrams clearly show that most of the barriers are common to multiple approaches and that most of the barriers relate to organisational concerns. The study gives a detailed map of the barriers that would help stakeholders from the AEC sector develop strategies to overcome the current obstacles in the shift to a CE.

Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.

The construction sector is the largest consumer of raw materials and accounts for 25%–40% of the total CO2 emissions globally. Besides, construction activities produce the highest amount of waste among all other sectors. According to the waste hierarchies, reuse is preferred to recycling; however, most of the recovery of construction and demolition wastes happens in the form of recycling and not reuse. Part of the recent efforts to promote the reuse rates includes estimating the reusability of the load-bearing building components to assist the stakeholders in making sound judgements of the reuse potentials at the end-of-life of a building and alleviate the uncertainties and perceived risks. This study aims to develop a probabilistic model using advanced supervised machine learning techniques (including random forest, K-Nearest Neighbours algorithm, Gaussian process, and support vector machine) to predict the reuse potential of structural elements at the end-of-life of a building. For this purpose, using an online questionnaire, this paper seeks the experts’ opinions with actual reuse experience in the building sector to assess the identified barriers by the authors in an earlier study. Furthermore, the results of the survey are used to develop an easy-to-understand learner for assessing the technical reusability of the structural elements at the end-of-life of a building. The results indicate that the most significant factors affecting the reuse of building structural components are design-related including, matching the design of the new building with the strength of the recovered element.

The United Arab Emirates’ (UAE) hot climate, coupled with the extreme demographic and urban growth experienced over the past four decades, has shaped a built environment where energetic quality of construction has been superseded by the quantity of construction needed to support the country's growth. This development is further aggravated by the slow development of energetic building codes, as well as the subsidized cost of electricity. The result is that the UAE consistently leads the list of countries with the highest environmental footprint, and the electricity production required to drive building cooling constitutes the brunt of the emissions balance-sheet. The work presented here reviews primarily UAE-based research that addresses the effectiveness of passive building-envelope measures that reduce energy consumption. A number of measures have been developed in response to the increasing demands of emerging energy regulations, and include measures specific to the building envelope in the planning phase or as retrofit, including radiative, convective and conductive heat transfer through walls, windows, roof, as well as energy efficient natural ventilation techniques. This review is geographically restricted to the UAE as its development challenges are directly tied to its distinct economic growth pattern and specific legislation implemented to address energy efficiency. Results confirm the importance of the following factors for energy-optimized structures: building orientation, thermal insulation (which can generate in excess of 20% energy savings in particular in the residential context), appropriate glazing type and orientation in highly glazed office buildings (up to 55% energy savings reported), excessive light levels and glare, and natural ventilation, which can reduce energy consumption from a reported high of 30% in villas to up to 79% in a high rise office building using mixed mode ventilation.

The energy efficiency of a building depends to a large measure on the characteristics of its envelope insulation. In the special case of internal gain dominated buildings, excessive building insulation may prevent the heat loss through the walls (anti-insulation effect), and thus generate the need for energy-intensive active systems to remove this thermal load. Detailed energetic simulations of a typical office building in Malaga (Spain), Dubai (UAE), and El Dorado (USA) show this anti-insulation effect and its dependency on climatic, constructive, and use factors. Results indicate that buildings in a predominantly cooling environment but within a certain range of heating degree days (HDD) will display this behavior: with very few to no HDD, the building’s energy consumption becomes insensitive to insulation increase (Dubai case); with a low number of HDDs the building becomes sensitive to anti-insulation (Malaga), and once a threshold is passed (El Dorado), the building’s energy consumption decreases with increased insulation. In order to further explore these limitations, simulations in 132 global locations of the building response to a step change in insulation are carried out. Results indicate that buildings in the Koppen climate zones Csa and Csb (Mediterranean climate), in locations with less than 2000 HDD and between 2000 and 5000 cooling degree days (CDD) are most susceptible to this anti-insulation behavior; however, quantitatively, the efficiency loss in these areas due to the insulation increase does not exceed 1 % of the overall energy consumption for the particular building studied and thus remains of limited importance.

Environmental sustainability considerations are slowly being integrated into governing criteria and regulations in industrial and urban development worldwide. A “cradle to grave” analysis increases understanding the implications of specific design options in the context of creating an environmentally sustainable product, however in commercial real estate the focus is generally on reducing cost, while long term operational and end-of-service considerations remain on a second plane of importance. The balance between initial costs and operational costs (environmental, economic and energetic) is directly reflected in the building energy use which, while requiring a higher initial investment, constitutes the principal driver in reducing the carbon footprint of the dwelling. But constructive measures that decrease operational energy use and thus also decrease operational greenhouse gas emissions require the use of more insulation materials. The embodied energy and GHG emissions associated with the full lifecycle of these additional materials needs to be included in the overall sustainability balance sheet of the development. This study shows that, in the particular case of the residential built environment of Dubai and the prevailing local electric power source generation mechanisms, the environmental sustainability cost of adding the insulation levels required to significantly mitigate transmission losses is small in comparison to the operational GHG emissions saved by their application. However, and in part due to typically short building lifetime and lack of comprehensive waste management strategies, the overall impact of using these materials within the full lifecycle of the Dubai built environment requires special consideration to end-of-service treatment.

Over the past decade Dubai's energy demand has increased in sync with the rapid urban development and population growth of the UAE. In particular the residential villa stock has grown by more than 300% from 20,000 villas in 2000 to over 60,000 villas in 2009. In order to limit energy consumption, the local authorities introduced building legislation (2001 and 2003) that prescribes minimum insulation levels for external walls and roofs. The resulting constructive solutions focus on the use of a mid-plane insulated prefabricated block to attain the prescribed maximum wall U value (0.57 W/m 2 K), however the reinforced concrete frame typically remains non-insulated, and thus introduces significant thermal bridges in the building envelope. This work investigates the impact of this thermal bridging effect on the building's energy consumption by modeling (hourly simulation using DesignBuilder/EnergyPlus) the energetic performance of a series of typically applied insulation strategies, both for buildings in the initial design stage, and in retrofit mode. The simulation model is calibrated against collected consumption data and experimental infiltration measurements of the actual building. Simulation results show that with appropriate external wall insulation strategies alone, energy savings of up to 30% are realized.