Management of thermal performance risks in buildings subject to climate change

This article reports on the use of building performance simulation to quantify the risks that climate change poses to the thermal performance of buildings, and to their critical functions. Through a number of case studies the article demonstrates that any prediction of the probable thermal building performance on the long timeframes inherent in climate change comes with very large uncertainties. The same cases are used to illustrate that assessing the consequences of predicted change is problematic, since the functions that the building provides in themselves often are a moving target. The article concludes that quantification of the risks posed by climate change is possible, but only with many restrictions. Further research that is needed to move to more effective discussion about risk acceptance and risk abatement for specific buildings is identified. © 2012 Elsevier Ltd.

Building performance simulation for the management of thermal performance risks in buildings subject to climate change

This paper reports on research that uses building performance simulation and uncertainty analysis to assess the risks that projected climate change poses to the thermal performance of buildings, and to their critical functions. The work takes meteorological climate change predictions as a starting point, but also takes into account developments and uncertainties in technology, occupancy, intervention and renovation, and others. Four cases are studied in depth to explore the prospects of the quantification of said climate change risks. The research concludes that quantification of the risks posed by climate change is possible, but only with many restrictive assumptions on the input side.

Avoiding pathologies in very deep networks

Choosing appropriate architectures and regularization strategies of deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.

The horizontal response of framed buildings on individual footings to excavation-induced movements

Deep excavations and tunnelling can cause ground movements that affect buildings within their influence zone. The current approach for building damage assessment is based on tensile strains estimated from the deflection ratio and the horizontal strains at the building foundation. This paper examines the significance of horizontal strains in buildings on individual footings. The first part of the paper presents a case study of a framed building in Singapore which was subjected to the effects of bored tunnelling, where significant horizontal strains were observed. The second part of the paper suggests a method to relate the horizontal strains induced in a building to the stiffness of the frame structure. Using a combination of simplified structural analysis and finite element models, design guidance is proposed to estimate excavation-induced horizontal strains in frame buildings on individual footings. © 2012 Taylor & Francis Group.

Mixing ventilation can reduce energy costs

The article provides information on a study on the potential of mixing ventilation in reducing energy costs in buildings such as theaters and schools. The study found that neither Manchester’s Contact Theatre and the Garrick Theatre in Lichfield in England is operating according to the displacement-ventilation principle upon which they were designed. Hybrid mixing ventilation has an important impact on both the ventilation rate and the thermal comfort of the theatres.

Future-proofed energy design for dwellings: Case studies from England and application to the Code for Sustainable Homes

This paper investigates 'future-proofing' as an unexplored yet all-important aspect in the design of low-energy dwellings. It refers particularly to adopting lifecycle thinking and accommodating risks and uncertainties in the selection of fabric energy efficiency measures and low or zero-carbon technologies. Based on a conceptual framework for future-proofed design, the paper first presents results from the analysis of two 'best practice' housing developments in England; i.e., North West Cambridge in Cambridge and West Carclaze and Baal in St. Austell, Cornwall. Second, it examines the 'Energy and CO2 Emissions' part of the Code for Sustainable Homes to reveal which design criteria and assessment methods can be practically integrated into this established building certification scheme so that it can become more dynamic and future-oriented.Practical application: Future-proofed construction is promoted implicitly within the increasingly stringent building regulations; however, there is no comprehensive method to readily incorporate futures thinking into the energy design of buildings. This study has a three-fold objective of relevance to the building industry:Illuminating the two key categories of long-term impacts in buildings, which are often erroneously treated interchangeably:- The environmental impact of buildings due to their long lifecycles.- The environment's impacts on buildings due to risks and uncertainties affecting the energy consumption by at least 2050. This refers to social, technological, economic, environmental and regulatory (predictable or unknown) trends and drivers of change, such as climate uncertainty, home-working, technology readiness etc.Encouraging future-proofing from an early planning stage to reduce the likelihood of a prematurely obsolete building design.Enhancing established building energy assessment methods (certification, modelling or audit tools) by integrating a set of future-oriented criteria into their methodologies. © 2012 The Chartered Institution of Building Services Engineers.

Towards productivity indicators for performance-based façade design in commercial buildings

Decision-making in the façade design process has a significant influence on several aspects of indoor environment, thereby making it a complex and multi-objective optimisation process. There are two principal barriers in the process of indentifying an optimal façade solution. Firstly, most existing indoor environmental evaluation methods do not account for all the indoor environmental quality (IEQ) aspects relevant to façade design. Secondly, the relationship between the physical properties of a particular façade design option and the resulting economic benefits accrued during its service-life is unknown. In this paper, we introduce the bases for establishing relationships between occupant productivity and the combinatorial effects of four key façade-related IEQ aspects, namely, thermal comfort, aural comfort, visual comfort and air quality, on occupant productivity. The proposed framework's potential is tested against seven existing experimental investigations and its applicability is illustrated by a simple façade design example. The proposed approach ultimately aims to provide a quantitative economic measure of alternative façade design options that would be applicable to early design stage. Aspects of the work that require further experimental validation are identified. © 2012 Elsevier Ltd.

A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method. © 2012 Elsevier B.V. All rights reserved.