Climate resilience of schools: a case study

Social and political concerns are frequently reflected in the design of school buildings, often in turn leading to the development of technical innovations. One example is a recurrent concern about the physical health of the nation, which has at several points over the last century prompted new design approaches to natural light and ventilation. The most critical concern of the current era is the global, rather than the indoor, environment. The resultant political focus on mitigating climate change has resulted in new regulations, and in turn considerable technical changes in building design and construction. The vanguard of this movement has again been in school buildings, set the highest targets for reducing operational carbon by the previous Government. The current austerity measures have moved the focus to the refurbishment and retrofit of existing buildings, in order to bring them up to the exacting new standards. Meanwhile there is little doubt that climate change is happening already, and that the impacts will be considerable. Climate scientists have increasing confidence in their predictions for the future; if today’s buildings are to be resilient to these changes, building designers will need to understand and design for the predicted climates in order to continue to provide comfortable and healthy spaces through the lifetimes of the buildings. This paper describes the decision processes, and the planned design measures, for adapting an existing school for future climates. The project is at St Faith’s School in Cambridge, and focuses on three separate buildings: a large Victorian block built as a substantial domestic dwelling in 1885, a smaller single storey 1970s block with a new extension, and an as-yet unbuilt single storey block designed to passivhaus principles and using environmentally friendly materials. The implications of climate change have been considered for the three particular issues of comfort, construction, and water, as set out in the report on Design for Future Climate: opportunities for adaptation in the built environment (Gething, 2010). The adaptation designs aim to ensure each of the three very different buildings remains fit for purpose throughout the 21st century, continuing to provide a healthy environment for the children. A forth issue, the reduction of carbon and the mitigation of other negative environmental impacts of the construction work, is also a fundamental aim for the school and the project team. Detailed modelling of both the operational and embodied energy and carbon of the design options is therefore being carried out, in order that the whole life carbon costs of the adaptation design options may be minimised. The project has been funded by the Technology Strategy Board as part of the Design for Future Climates programme; the interdisciplinary team includes the designers working on the current school building projects and the school bursar, supported by researchers from the University of Cambridge Centre for Sustainable Development. It is hoped that lessons from the design process, as well as the solutions themselves, will be transferable to other buildings in similar climatic regions.

Unsupervised intra-lingual and cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction

Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper first presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Second, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Third, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation.

Social sensing in the field: Challenges in detecting social interactions in construction sites

Construction industry is a sector that is renowned for the slow uptake of new technologies. This is usually due to the conservative nature of this sector that relies heavily on tried and tested and successful old business practices. However, there is an eagerness in this industry to adopt Building Information Modelling (BIM) technologies to capture and record accurate information about a building project. But vast amounts of information and knowledge about the construction process is typically hidden within informal social interactions that take place in the work environment. In this paper we present a vision where smartphones and tablet devices carried by construction workers are used to capture the interaction and communication between workers in the field. Informal chats about decisions taken in the field, impromptu formation of teams, identification of key persons for certain tasks, and tracking the flow of information across the project community, are some pieces of information that could be captured by employing social sensing in the field. This information can not only be used during the construction to improve the site processes but it can also be exploited by the end user during maintenance of the building. We highlight the challenges that need to be overcome for this mobile and social sensing system to become a reality. © 2012 ACM.

Detection of Construction Workers in Video Frames for Automatic Initialization of Vision Trackers

Vision based tracking can provide the spatial location of construction entities such as equipment, workers, and materials in large scale, congested construction sites. It tracks entities in video streams by inferring their locations based on the entities’ visual features and motion histories. To initiate the process, it is necessary to determine the pixel areas corresponding to the construction entities to be tracked in the following consecutive video frames. In order to fully automate the process, an automated way of initialization is needed. This paper presents the method for construction worker detection which can automatically recognize and localize construction workers in video frames. The method first finds the foreground areas of moving objects using a background subtraction method. Within these foreground areas, construction workers are recognized based on the histogram of oriented gradients (HOG) and histogram of the HSV colors. HOG’s have proved to work effectively for detection of people, and the histogram of HSV colors helps differentiate between pedestrians and construction workers wearing safety vests. Preliminary experiments show that the proposed method has the potential to automate the initialization process of vision based tracking.

Management and Analysis of Unstructured Construction Data Types

Compared with structured data sources that are usually stored and analyzed in spreadsheets, relational databases, and single data tables, unstructured construction data sources such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our vision for data management and mining addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data mining on text-based, web-based, image-based, and network-based construction databases.

Data Analysis on Complicated Construction Data Sources: Vision, Research, and Recent Developments

Compared with construction data sources that are usually stored and analyzed in spreadsheets and single data tables, data sources with more complicated structures, such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our definition and vision for advanced data analysis addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data analysis on text-based, image-based, web-based, and network-based construction sources. It is shown in this paper that particular data preparation, representation, and analysis operations should be identified, and integrated with careful problem investigations and scientific validation measures in order to provide general frameworks in support of information search and knowledge discovery from such information-abundant data sources.