Construction practitioners’ perception of key drivers of reputation in mega-construction projects

Purpose: The purpose of this study is to commence the discourse on the non-inclusiveness of the dynamics of reputation within the construction industry by identifying and examining the key product and process drivers of reputation in mega-construction projects. Design/methodology/approach: Data was collected through an exploratory sequential mixed methods approach which commences with a qualitative study and culminates with a quantitative study to identify product and process drivers of reputation in mega-construction projects. Findings: The findings suggest that “project quality”, “robust social and environmental sustainability plan”, “project team competence and interpersonal relationship” and “project process efficacy” are the four key drivers influencing the reputation of mega-construction projects. Research limitations/implications: The findings of this study are solely based on the perception of UK construction practitioners; therefore, the results may only be considered valid in this context. The identification of these key drivers provides a pathway where stakeholders, professionals and organisations can identify and prioritise critical issues associated with enhancing and sustaining the reputation of mega-construction projects. Originality/value: Findings of this research make a significant contribution to the discourse on the concept of reputation within the construction industry by identifying its specific drivers of reputation

Performance comparison of deep learning and boosted trees for cryptocurrency closing price prediction

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial heterogeneous datasets characterized by complex interactions between predictors, which may be difficult for conventional techniques to achieve optimal results. In addition, volatility significantly impacts investment decisions; thus, investors are confronted with how to determine the price and assess their financial investment risks reasonably. This study investigates the performance evaluation of a genetic algorithm tuned Deep Learning (DL) and boosted tree-based techniques to predict several cryptocurrencies' closing prices. The DL models include Convolutional Neural Networks (CNN), Deep Forward Neural Networks, and Gated Recurrent Units. The study assesses the performance of the DL models with boosted tree-based models on six cryptocurrency datasets from multiple data sources using relevant performance metrics. The results reveal that the CNN model has the least mean average percentage error of 0.08 and produces a consistent and highest explained variance score of 0.96 (on average) compared to other models. Hence, CNN is more reliable with limited training data and easily generalizable for predicting several cryptocurrencies' daily closing prices. Also, the results will help practitioners obtain a better understanding of crypto market challenges and offer practical strategies to lower risks

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

Offsite construction for emergencies: A focus on Isolation Space Creation (ISC) measures for the COVID-19 pandemic

The outbreak of a pandemic of global concern, the Corona Virus Disease 2019 (COVID-19) has tested the capacity of healthcare facilities to the brim in many developed countries. In a minacious fashion of rapid spread and extreme transmission rate, COVID-19 has triggered a shortage of healthcare facilities such as hospital bed spaces and ventilators. Various strategies have been adopted by the worst-hit countries to slacken or halt the spread of the virus. Common Isolation Space Creation (ISC) measures for the COVID-19 pandemic containment includes self-isolation at home, isolation at regular hospitals, isolation at existing epidemic hospitals, isolation at retrofitted buildings for an emergency, isolation at Temporary Mobile Cabins (TMCs), isolation at newly constructed temporary hospitals for COVID-19. This study evaluates the ISC measures and proposes offsite and modular solutions for the construction industry and built environment to respond to emergencies. While this study has proposed a solution for creating emergency isolation spaces for effective containment of such pandemic, other critical COVID-19 challenges such as the shortage of healthcare staff and other facilities are not addressed in this study

Two-stage capacity optimization approach of multi-energy system considering its optimal operation

With the depletion of fossil fuel and climate change, multi-energy systems have attracted widespread attention in buildings. Multi-energy systems, fuelled by renewable energy, including solar and biomass energy, are gaining increasing adoption in commercial buildings. Most of previous capacity design approaches are formulated based upon conventional operating schedules, which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system. Therefore, a two-stage capacity optimization approach is proposed for the multi-energy system with its optimal operating schedule taken into consideration. To demonstrate the effectiveness of the proposed capacity optimization approach, it is tested on a renewable energy fuelled multi-energy system in a commercial building. The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit, heat recovery unit, heat exchanger, absorption chiller, electric chiller, biomass boiler, building integrated photovoltaic and photovoltaic thermal hybrid solar collector. The variable efficiency owing to weather condition and part-load operation is also considered. Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization, respectively. The two optimization stages are interrelated; thus, the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively. With the adoption of the proposed novel capacity optimization approach, there is a 14% reduction of year-round biomass consumption compared to one with the conventional capacity design approach

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

A Big Data analytics approach for construction firms failure prediction models

Using 693,000 datacells from 33,000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine (SVM), multiple discriminant analysis (MDA) and logistic regression (LR). The accuracy of the models on test data surprisingly showed ANN to have only a slightly better accuracy than LR and MDA. The ANN’s number of units in the hidden layer and weight decay hyperparameters were consequently tuned using the grid search. Tuning process led to tedious machine computation that was aborted after many hours without completion. The state of art Big Data Analytics (BDA) technology was, for the first time in failure prediction, consequently employed and the tuning was completed in some seconds. Mean accuracy from cross-validation was used for selection of the model with best parameter values which were used to develop a new ANN model which outperformed all previously developed models on test data. Subsequent use of selected variables to develop new models led to reduced tuning computational cost but not improved performance. Since the real-life effect of a misclassification cost is greater than the tedious computation cost, it was concluded that BDA is the best compromise

Critical factors for insolvency prediction: Towards a theoretical model for the construction industry

© 2016 Informa UK Limited, trading as Taylor & Francis Group. Many construction industry insolvency prediction model (CI-IPM) studies have arbitrarily employed or simply adopted from previous studies different insolvency factors, without justification, leading to poorly performing CI-IPMs. This is due to the absence of a framework for selection of relevant factors. To identify the most important insolvency factors for a high-performance CI-IPM, this study used three approaches. Firstly, systematic review was used to identify all existing factors. Secondly, frequency of factor use and accuracy of models in the reviewed studies were analysed to establish the important factors. Finally, using a questionnaire survey of CI professionals, the importance levels of factors were validated using the Cronbach's alpha reliability coefficient and significant index ranking. The findings show that the important quantitative factors are profitability, liquidity, leverage, management efficiency and cash flow. While important qualitative factors are management/owner characteristics, internal strategy, management decision making, macroeconomic firm characteristics and sustainability. These factors, which align with existing insolvency-related theories, including Porter's five competitive forces and Mintzberg's 5Ps (plan, ploy, pattern, position and perspective) of strategy, were used to develop a theoretical framework. This study contributes to the debate on the need to amalgamate qualitative and quantitative factors to develop a valid CI-IPM