Purpose: Buildings and their use is a complex process from design to occupation. Buildings produce huge volumes of data such as building information modelling (BIM), sensor (e.g. from building management systems), occupant and building maintenance data. These data can be spread across multiple disconnected systems in numerous formats, making their combined analysis difficult. The purpose of this paper is to bring these sources of data together, to provide a more complete account of a building and, consequently, a more comprehensive basis for understanding and managing its performance. Design/methodology/approach: Building data from a sample of newly constructed housing units were analysed, several properties were identified for the study and sensors deployed. A sensor agnostic platform for visualising real-time building performance data was developed. Findings: Data sources from both sensor data and qualitative questionnaire were analysed and a matrix of elements affecting building performance in areas such as energy use, comfort use, integration with technology was presented. In addition, a prototype sensor visualisation platform was designed to connect in-use performance data to BIM. Originality/value: This work presents initial findings from a post occupancy evaluation utilising sensor data. The work attempts to address the issues of BIM in-use scenarios for housing sector. A prototype was developed which can be fully developed and replicated to wider housing projects. The findings can better address how indoor thermal comfort parameters can be used to improve housing stock and even address elements such as machine learning for better buildings.

Journal of Information Technology in Construction

Automated deep neural architecture generation has gained increasing attention. However, exiting studies either optimize important design choices, without taking advantage of modern strategies such as residual/dense connections, or they optimize residual/dense networks but reduce search space by eliminating fine-grained network setting choices. To address the aforementioned weaknesses, we propose a novel particle swarm optimization (PSO)-based deep architecture generation algorithm, to devise deep networks with residual connections, whilst performing a thorough search which optimizes important design choices. A PSO variant is proposed which incorporates a new encoding scheme and a new search mechanism guided by non-uniformly randomly selected neighboring and global promising solutions for the search of optimal architectures. Specifically, the proposed encoding scheme is able to describe convolutional neural network architecture configurations with residual connections. Evaluated using benchmark datasets, the proposed model outperforms existing state-of-the-art methods for architecture generation. Owing to the guidance of diverse non-uniformly selected neighboring promising solutions in combination with the swarm leader at fine-grained and global levels, the proposed model produces a rich assortment of residual architectures with great diversity. Our devised networks show better capabilities in tackling vanishing gradients with up to 4.34% improvement of mean accuracy in comparison with those of existing studies.

Construction Design Management (DM) involves the coordination of design processes and activities to ensure quality project design, yet it involves many challenges. This study reports on a collaborative Knowledge Transfer Partnership (KTP) project with a Case Study Organization (CSO) that tackled several issues faced by construction design managers. Employing a design science research methodology (DSRM), qualitative analysis of semi-structured interviews with purposefully identified design managers uncovered real-world concerns around design co-ordination and performance monitoring. To address these concerns, a web-based design management prototype dashboard was developed using typical project data to aid in the management of design coordination, task prioritization, and reporting functionalities. The web-based Design Management prototype dashboard enhances design management productivity in construction firms by monitoring design production, assessing designer performance trends, and focusing on Technical Queries (TQs) and Requests for Information (RFIs). Digitalizing selected design management processes improves efficiency and productivity. The visual reporting of the dashboard enables design production monitoring at project and portfolio levels, assesses trends in designer performance, and maintains focus on TQs and RFIs. Demonstrating how web-based Design Management dashboards can enhance productivity, this study emphasizes practical solutions derived from employing a design science research methodology. The development and application of the web-based dashboard contribute to the growing evidence that employing design science research methodology in construction can yield tangible solutions to address real-world construction concerns.

Current research on Digital Twin (DT) is largely focused on the performance of built assets in their operational phases as well as on urban environment. However, Digital Twin has not been given enough attention to construction phases, for which this paper proposes a Digital Twin framework for the construction phase, develops a DT prototype and tests it for the use case of measuring the productivity and monitoring of earthwork operation. The DT framework and its prototype are underpinned by the principles of versatility, scalability, usability and automation to enable the DT to fulfil the requirements of large-sized earthwork projects and the dynamic nature of their operation. Cloud computing and dashboard visualisation were deployed to enable automated and repeatable data pipelines and data analytics at scale and to provide insights in near-real time. The testing of the DT prototype in a motorway project in the Northeast of England successfully demonstrated its ability to produce key insights by using the following approaches: (i) To predict equipment utilisation ratios and productivities; (ii) To detect the percentage of time spent on different tasks (i.e., loading, hauling, dumping, returning or idling), the distance travelled by equipment over time and the speed distribution; and (iii) To visualise certain earthwork operations.

Memoryscapes presents a detailed case-study of a design-led inquiry concerning the development of immersive experiences to support city-center urban redevelopment, to encourage visiting and touristic activity. Our approach sought to explore how we might make innovative use of local heritage assets, within these digital experiences, to enhance engagement with place. We brought historians, “placemakers”, design-creatives, and technology-developers into dialogue, in a series of co-design sessions (over 9 months, through 6 workshops and with 77 participants). Our initial workshop allowed us to explore and develop extant theories of trajectories and immersive experiences, which led to the development of a design toolkit. The toolkit facilitated further co-design sessions with stakeholder communities. This paper contributes a deepening and nuancing of theory of trajectories by applying it in a specific context, namely heritage-led immersive experiences for use in urban areas; and provides critical reflections on the design toolkit to support interdisciplinary stakeholder development of immersive urban experiences.