Data transfer between digital models of built assets and their operation & maintenance systems

The operation and maintenance of built assets is crucial for optimising their whole life cost and efficiency. Historically, however, there has been a general failure in the transfer information between the design-and-construct (D&C) and operate-and-maintain (O&M) phases of the asset lifecycle. The recent steady uptake of digital technologies, such as Building Information Modelling (BIM) in the D&C phase has been accompanied by an expectation that this would enable better transfer of information to those responsible for O&M. Progress has been slow, with practitioners being unsure as to how to incorporate BIM into their working practices. Three types of challenge are identified, related to communication, experience and technology. In examining the last aspect, it appears that a major problem has been that of interoperability between building information models and the many computer-aided facilities management (CAFM) systems in use. The successful and automatic transfer of information from a building model to an FM tool is, in theory, achievable through the medium of the Industry Foundation Classes (IFC) schema. However, this relies upon the authoring of the model in terms of how well its structure permits the identification of relevant objects, their relationships and attributes. The testing of over 100 anonymised building models revealed that very few did; prohibiting their straightforward mapping to the maintenance database we had selected for the test. An alternative, hybrid approach was developed using an open-source software toolkit to identify objects by their geometry as well as their classification, thus enabling their automatic transfer. In some cases, manual transfer proved necessary. The implications are that while these problems can be overcome on a case-by-case basis, interoperability between D&C and O&M systems will not become standard until it is accommodated by appropriate and informed authoring of building models

Smart Connected Homes: Integrating Sensor, Occupant and BIM data for Building Performance Analysis

Buildings produce huge volumes of data such as BIM, sensor, occupant and building maintenance data. Data is spread across multiple disconnected systems in numerous formats, making it difficult to identify performance gaps between building design and use. Better methods for gathering and analysing data can be used to support building managers with managing building performance. The knowledge can also be fed back to designers and contractors to help close the performance gaps. We have developed a platform to integrate BIM, sensor and occupant data for providing actionable advice for building managers. A social housing organisation is acting as a use case for the platform. A methodology for developing the information needs to support data capture across disconnected systems is proposed and the challenges of bringing data-sets together to provide meaningful information to building owners and managers are presented

Benchmarking BIM Levels of Training and Education amongst Construction Management Practitioners

UK Government created a strategic deadline of 2016 for the adoption and use of Level 2 BIM on all centrally procured projects. A shift from Computer Aided Design (CAD) to Building Information Modelling (BIM) has been driven by the need to improve the way that the industry delivers projects. It is believed that BIM better facilitates opportunities for collaboration and project enhancement than traditional project information management processes. It is also thought that by improving the quality of information, and adopting a more collaborative approach through a model- based design industry such advancements can be made. The originality of this research is in developing an understanding of the current-status of BIM training and education amongst construction management practitioners. The present research uses a quantitative survey approach to investigate the current-status of BIM awareness, understanding, use, and perceptions towards readiness for the 2016 mandate. Results highlight that approximately half of the sample have received some kind of education or training although there were higher levels of BIM awareness, use and understanding. Investigations also reveal that the majority of training and education received by practitioners is self-sourced, but amongst those respondents who have not received any education or training there are expectations that employers should provide these

Incorporating Emerging Technologies in the Forensic Analysis of Construction Project Delays

Considering the significant role of the construction industry in the global economy, its continuous adoption of new technological advances is both desirable and inevitable. These advances include Building Information Modelling (BIM) and Artificial Intelligence (AI)/Machine Learning (ML). However, not all sections of the industry currently embrace these developments. Forensic Delay Analysis (FDA) is an activity of specialists in extracting and presenting evidence contractual claims disputes that relate to project delays. Such delays are frequent and expensive, but the FDA process has benefitted little from these new technologies. The paper reports the initial work of a collaborative PhD project funded under the Intensive Industrial Innovation Programme of the European Regional Development Fund. The project explores the integration of BIM and AI/ML technologies within the FDA process. The potential of emerging technologies in different parts of the FDA process is first considered, followed by a systematic literature review (SLR) of published work that might support, refute, or exemplify such contributions. The findings show that BIM and AI/ML offer promising solutions to the current challenges of FDA and opportunities for enhancing the effectiveness of dispute resolution, but further work is needed to test the proposed improvements on real-world project workflows and to collect expert feedback to assess their effectiveness

Automating equipment productivity measurement using deep learning

Measuring the productivity of earth moving equipment help to identify their inefficiencies and improve their performance; however, measurement processes are time and resource intensive. Current literature has foccussed on automating equipment activity capture but still lack adequate approaches for measurement of equipment productivity rates. Our contribution is to present a methodology for automating equipment productivity measurement using kinematic and noise data collected through smartphone sensors from within equipment and deep learning algorithms for recognizing equipment states. The testing of the proposed method in a real world case study demonstrated very high accuracy of 99.78 in measuring productivity of an excavator

Domestic Widgets: Leveraging Household Creativity in Co-Creating Data Physicalisations

The home environment is a complex design space, especially when it has multiple inhabitants. As such, the home presents challenges for the design of smart products. Householders may be different ages and have differing interests, needs, and attitudes towards technology. We pursued a research-through-design study with family households to envision and ‘co-create’ the future of data-enabled artifacts for their homes. We have iteratively developed domestic research artefacts for these households that are open, data-enabled, physical visualizations. These artefacts - called Domestic Widgets - are customisable in their design and functionality throughout their lifespan. The development process highlights design challenges for sustained co-creation and the leveraging of household creativity in (co-creation) research toolkits. These include the need to allow and inspire iterative customization, the need to accommodate changing roles within the home ecology, and the aim that such design should be inclusive for all family members (irrespective of age and technical proficiency), whilst maintaining a role and purpose in the home. We invite the RTD community to critically discuss our, and other, open and iterative end-user designs for sustained co-creation. By presenting unbuilt and interactive pre-built Domestic Widgets, we interactively foster engagement with practises of sustained co-creation

Digitally-Enabled Design Management

Calls for the digital transformation of the construction sector in part revolve around a need for productivity improvements, with a focus upon project time and cost enhancements. The purpose of this work is to provide a state-of-the-art analysis of design management (DM) usually employed to oversee design quality by coordinating design information, typically on behalf of a construction contractor. DM methods, activities, and processes with respect to the potential and underutilisation of building information modelling (BIM) are discussed. A synthesis of recent research efforts is provided identifying further emerging, disruptive, but underutilised digital tools and technologies, which when integrated with BIM, are capable of supporting DM processes. This chapter will aid practitioners and researchers in the design, implementation, and management of digital tools, and provide greater support to the DM function on modern construction projects. It will also be of use to students for a grounding in BIM and BIM-related technologies

Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization

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

Measuring and benchmarking the productivity of excavators in infrastructure projects: A deep neural network approach

Inefficiencies in the management of earthmoving equipment greatly contribute to the productivity gap of infrastructure projects. This paper develops and tests a Deep Neural Network (DNN) model for estimating the productivity of excavators and establishing a productivity measure for their benchmark. After investigating current practices for measuring the productivity of earthwork equipment during 13 interviews with selected industry experts, the DNN model was developed and tested in one of the ‘High Speed rail second phase’ (HS2) sites. The accuracy of prediction achieved by the DNN model was evaluated using the coefficient of determination (R2) and the Weighted Absolute Percentage Error (WAPE) resulting in 0.87 and 69.64%, respectively. This is an adequate level of accuracy when compared to other similar studies. However, according to the WAPE method, the accuracy is still 10.36% below the threshold (i.e. 80%) expected by the industry experts. An inspection of the prediction results over the testing period (21 days) revealed better precision in days with high excavation volumes compared to days with low excavation volumes. This was attributed to the likely involvement of manual work (i.e. archaeologists in the case of the selected site) alongside some of the excavators, which caused gaps in telematics data. This indicates that the accuracy attained is adequate, but the proposed approach is more accurate in a highly mechanised environment (i.e. excavation work with equipment predominantly and limited manual interventions) compared to a mixed mechanised-manual working environment. A bottom-up benchmark measure (i.e. excavation rate) that can be used to measure and benchmark the excavation performance of an individual or a group of equipment, through a work area, to a whole site was also proposed and discussed

Software Simulation for Preparing Emergency Response Teams in Dealing with Incidents within the Gas Infrastructure

This research is working in collaboration with a UK gas infrastructure provider to conduct a collaborative study. It looks at an uncontrolled event that requires a response outside the routine that occurs as a result of transient work activity. The resulting response is required from multiple agencies: Emergency services, utilities, Local Authorities etc. Such category two responders are covered by various bodies as well as health and safety legislation: the UK Health and Safety Executive who are responsible for planning and prevention of major incidents under the Control Of Major Accident Hazards regulations 1999 (COMAH) and Pipeline Safety Regulations 1996 (PSR). This legislation provides guidance for planning and prevention of major incidents. Therefore the above bodies must prepare emergency response plans, review and test emergency response plans with emergency response teams every 3 years and provide evidence of plans and testing to UK Health and Safety Executive. The testing can take different forms such as tabletop role playing exercises, which disseminate information about plan with other agencies and highlight amendments required to the plan whilst encouraging communication between agencies and highlighting issues to other agencies. The research provides a case study of current industry practice for planning and preparing for incidents involving high pressure gas pipelines. It also investigates the use of software simulation in other industries and what use software simulation brings to conducting the exercises, through the use of multi-player activities conducted across multiple sites simultaneously as a training tool, providing opportunities to participate in exercises even if a participant can‟t attend the exercise on the day it is held. It provides an audit trail of attendees and exercise details for HSE and acts as a repository for multiple scenarios that can be altered overtime to reflect changes in the scenario environment. Software also provides the ability to pick scenarios from other exercises to make up a new exercise, saving time and money on developing new scenarios. Simulation of Control/Command room scenarios is currently used by the military, medicine, emergency services, aerospace, flight, marine and automobile companies, all of which would have common elements, which are a multi-player role playing environment with exercises made up of scenarios using audio and visual resources in both 2D and 3D with the ability to record participant responses. Software therefore has the potential to preparing emergency responders for incidents involving the gas infrastructure