Digital Twins for the built environment: Learning from conceptual and process models in manufacturing

The overall aim of this paper is to contribute to a better understanding of the Digital Twin (DT) paradigm in the built environment by drawing inspiration from existing DT research in manufacturing. The DT is a Product Life Management information construct that has migrated to the built environment while research on the subject has grown intensely in recent years. Common to early research phases, DT research in the built environment has developed organically, setting the basis for mature definitions and robust research frameworks. As DT research in manufacturing is the most developed, this paper seeks to advance the understanding of DTs in the built environment by analysing how the DT systems reported in manufacturing literature are structured and how they function. Firstly, this paper presents a thorough review and a comparison of DT, cyber-physical systems (CPS), and building information modelling (BIM). Then, the results of the review and categorisation of DT structural and functional descriptions are presented. Fifty-four academic publications and industry reports were reviewed, and their structural and functional descriptions were analysed in detail. Three types of structural models (i.e. conceptual models, system architectures, and data models) and three types of functional models (process and communication models) were identified. DT maturity models were reviewed as well. From the reviewed descriptions, four categories of DT conceptual models (prototypical, model-based, interface-oriented, and service-based) and six categories of DT process models (DT creation, DT synchronisation, asset monitoring, prognosis and simulation, optimal operations, and optimised design) were defined and its applicability to the AECO assessed. While model-based and service-based models are the most applicable to the built environment, amendments are still required. Prognosis and simulation process models are the most widely applicable for AECO use-cases. The main contribution to knowledge of this study is that it compiles the DT’s structural and functional descriptions used in manufacturing and it provides the basis to develop DT conceptual and process models specific to requirements of the built environment sectors

Augmented and virtual reality in construction: Drivers and limitations for industry adoption

Augmented and virtual reality have the potential to provide a step-change in productivity in the construction sector; however, the level of adoption is very low. This paper presents a systematic study of the factors that limit and drive adoption in a construction sector-specific context. A mixed research method was employed, combining qualitative and quantitative data collection and analysis. Eight focus groups with 54 experts and an online questionnaire were conducted. Forty-two limiting and driving factors were identified and ranked. Principal component analysis was conducted to group the identified factors into a smaller number of factors based on correlations. Four types of limiting factors and four types of driving factors were identified. The main limitation of adoption is that AR and VR technologies are regarded as expensive and immature technologies that are not suitable for engineering and construction. The main drivers are that AR and VR enable improvements in project delivery and provision of new and better services. This study provides valuable insights to stakeholders to devise actions that mitigate the limiting factors and that boost the driving factors. This is one of the first systematic studies to present a detailed analysis of the factors that limit and drive adoption of AR and VR in the construction industry. The main contribution of this study is that it grouped and characterized myriad limiting and driving factors into easily understandable categories, so that the limiting factors can be effectively mitigated and the driving factors potentiated. A roadmap with specific short-term and medium-term actions for improving adoption was outlined

BIM data model requirements for asset monitoring and the circular economy

© 2020, Emerald Publishing Limited. Purpose: The purpose of this paper is to review and provide recommendations to extend the current open standard data models for describing monitoring systems and circular economy precepts for built assets. Open standard data models enable robust and efficient data exchange which underpins the successful implementation of a circular economy. One of the largest opportunities to reduce the total life cycle cost of a built asset is to use the building information modelling (BIM) approach during the operational phase because it represents the largest share of the entire cost. BIM models that represent the actual conditions and performance of the constructed assets can boost the benefits of the installed monitoring systems and reduce maintenance and operational costs. Design/methodology/approach: This paper presents a horizontal investigation of current BIM data models and their use for describing circular economy principles and performance monitoring of built assets. Based on the investigation, an extension to the industry foundation classes (IFC) specification, recommendations and guidelines are presented which enable to describe circular economy principles and asset monitoring using IFC. Findings: Current open BIM data models are not sufficiently mature yet. This limits the interoperability of the BIM approach and the implementation of circular economy principles. An overarching approach to extend the current standards is necessary, which considers aspects related to not only modelling the monitoring system but also data management and analysis. Originality/value: To the authors’ best knowledge, this is the first study that identifies requirements for data model standards in the context current linear economic model of making, using and disposing is growing unsustainably far beyond the finite limits of planet of a circular economy. The results of this study set the basis for the extension of current standards required to apply the circular economy precepts

Big data analytics system for costing power transmission projects

© 2019 American Society of Civil Engineers. Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used widely in practice. The purpose of this study is to leverage a big data architecture, to manage the large and diverse data required for predictive analytics. This paper presents a predictive analytics and modeling system (PAMS) that facilitates the use of different data-driven cost prediction methods. A 2.75-million-point dataset of power transmission projects has been used as a case study. The proposed big data architecture fits this purpose. It can handle the diverse datasets used in the construction sector. The three most prevalent cost estimation models were implemented (linear regression, support vector regression, and artificial neural networks). All models performed better than the estimated human-level performance. The primary contribution of this study to the body of knowledge is an empirical indication that data-driven methods analysed in this study are on average 13.5% better than manual methods for cost estimation of power transmission projects. Additionally, the paper presents a big data architecture that can manage and process large varied datasets and seamless scalability

Structural Performance Monitoring Using a Dynamic Data-Driven BIM Environment

Structural health monitoring data has not been fully leveraged to support asset management due to a lack of effective integration with other datasets. A Building Information Modelling (BIM) approach is presented to leverage structural monitoring data in a dynamic manner. The approach allows for the automatic generation of parametric BIM models of structural monitoring systems that include time-series sensor data; and it enables data-driven and dynamic visualisation in an interactive 3D environment. The approach supports dynamic visualisation of key structural performance parameters, allows for the seamless updating and long-term management of data, and facilitates data exchange by generating Industry Foundation Classes (IFC) compliant models. A newly-constructed bridge near Stafford, UK, with an integrated fibre-optic sensor based monitoring system was used to test the capabilities of the developed approach. The case study demonstrated how the developed approach facilitates more intuitive data interpretation, provides a user-friendly interface to communicate with various stakeholders, allows for the identification of malfunctioning sensors thus contributing to the assessment of monitoring system durability, and forms the basis for a powerful data-driven asset management tool. In addition, this project highlights the potential benefits of investing in the development of data-driven and dynamic BIM environments

Robotics and automated systems in construction: Understanding industry-specific challenges for adoption

© 2019 The Authors The construction industry is a major economic sector, but it is plagued with inefficiencies and low productivity. Robotics and automated systems have the potential to address these shortcomings; however, the level of adoption in the construction industry is very low. This paper presents an investigation into the industry-specific factors that limit the adoption in the construction industry. A mixed research method was employed combining literature review, qualitative and quantitative data collection and analysis. Three focus groups with 28 experts and an online questionnaire were conducted. Principal component and correlation analyses were conducted to group the identified factors and find hidden correlations. The main identified challenges were grouped into four categories and ranked in order of importance: contractor-side economic factors, client-side economic factors, technical and work-culture factors, and weak business case factors. No strong correlation was found among factors. This study will help stakeholders to understand the main industry-specific factors limiting the adoption of robotics and automated systems in the construction industry. The presented findings will support stakeholders to devise mitigation strategies

Disassembly and deconstruction analytics system (D-DAS) for construction in a circular economy

© 2019 Despite the relevance of building information modelling for simulating building performance at various life cycle stages, Its use for assessing the end-of-life impacts is not a common practice. Even though the global sustainability and circular economy agendas require that buildings must have minimal impact on the environment across the entire lifecycle. In this study therefore, a disassembly and deconstruction analytics system is developed to provide buildings’ end-of-life performance assessment from the design stage. The system architecture builds on the existing building information modelling capabilities in managing building design and construction process. The architecture is made up of four different layers namely (i) Data storage layer, (ii) Semantic layer, (iii) Analytics and functional models layer and (iv) Application layer. The four layers are logically connected to function as a single system. Three key functionalities of the disassembly and deconstruction analytics system namely (i) Building Whole Life Performance Analytics (ii) Building Element Deconstruction Analytics and (iii) Design for Deconstruction Advisor are implemented as plug-in in Revit 2017. Three scenarios of a case study building design were used to test and evaluate the performance of the system. The results show that building information modelling software capabilities can be extended to provide a platform for assessing the performance of building designs in respect of the circular economy principle of keeping the embodied energy of materials perpetually in an economy. The disassembly and deconstruction analytics system would ensure that buildings are designed with design for disassembly and deconstruction principles that guarantee efficient materials recovery in mind. The disassembly and deconstruction analytics tool could also serve as a decision support platform that government and planners can use to evaluate the level of compliance of building designs to circular economy and sustainability requirements

Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations

© 2020 Elsevier Ltd Forecasting imminent accidents in power infrastructure projects require a robust and accurate prediction model to trigger a proactive strategy for risk mitigation. Unfortunately, getting ready-made machine learning algorithms to eliminate redundant features optimally is challenging, especially if the parameters of these algorithms are not tuned. In this study, a particle swarm optimization is proposed both for feature selection and parameters tuning of the gradient boosting machine technique on 1,349,239 data points of an incident dataset. The predictive ability of the proposed method compared to conventional tree-based methods revealed near-perfect predictions of the proposed model on test data (classification accuracy − 0.878 and coefficient of determination − 0.93) for the two outcome variables ACCIDENT and INJURYFREQ. The high predictive power obtained reveals that injuries do not occur in a chaotic fashion, but that underlying patterns and trends exist that can be uncovered and captured via machine learning when applied to sufficiently large datasets. Also, key relationships identified will assist safety managers to understand possible risk combinations that cause accidents; helping to trigger proactive risk mitigation plans

Investigating profitability performance of construction projects using big data: A project analytics approach

© 2019 The Authors The construction industry generates different types of data from the project inception stage to project delivery. This data comes in various forms and formats which surpass the data management, integration and analysis capabilities of existing project intelligence tools used within the industry. Several tasks in the project lifecycle bear implications for the efficient planning and delivery of construction projects. Setting up right profit margins and its continuous tracking as projects progress are vital management tasks that require data-driven decision support. Existing profit estimation measures use a company or industry wide benchmarks to guide these decisions. These benchmarks are oftentimes unreliable as they do not factor in project-specific variations. As a result, projects are wrongly estimated using uniform rates that eventually end up with entirely unusual margins either due to underspends or overruns. This study proposed a project analytics approach where Big Data is harnessed to understand the profitability distribution of different types of construction projects. To this end, Big Data architecture is recommended, and a prototype implementation is shown to store and analyse large amounts of projects data. Our data analysis revealed that profit margins evolve, and the profitability performance varies across several project attributes. These insights shall be incorporated as knowledge to machine learning algorithms to predict project margins accurately. The proposed approach enabled the fast exploration of data to understand the underlying pattern in the profitability performance for different types of construction projects

Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting

Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications