The construction industry is a major economic sector, but it plagued with inefficiencies and low productivity. Robotics automated systems have the potential to address these shortcomings; however, level of adoption in very low. This paper presents an investigation into industry-specific factors that limit industry. A mixed research method was employed combining literature review, qualitative quantitative data collection analysis. Three focus groups 28 experts online questionnaire were...

The aim of this study is to develop a BIM-based Whole-life Performance Estimator (BWPE) for appraising the salvage performance structural components buildings right from design stage. A review extant literature was carried out identify factors that influence during their useful life. Thereafter, mathematical modelling approach adopted BWPE using identified and principle/concept Weibull reliability distribution manufactured products. model implemented in Building Information Modelling (BIM)...

Cloud computing technologies have revolutionised several industries for years. Although the construction industry is well placed to leverage these competitive and operational advantage, diffusion of in follows a steep curve. This study therefore highlights current contributions use cases cloud practices. As such, systematic review was carried out using ninety-two (92) peer-reviewed publications, published between 2009 2019. A key highlight findings that an innovation delivery enabler other...

Despite the relevance of building information modelling for simulating performance at various life cycle stages, Its use assessing end-of-life impacts is not a common practice. Even though global sustainability and circular economy agendas require that buildings must have minimal impact on environment across entire lifecycle. In this study therefore, disassembly deconstruction analytics system developed to provide buildings’ assessment from design stage. The architecture builds existing...

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase market capitalization and trade volume. However, the cryptocurrency is highly volatile burdened 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 decisions; thus, investors are confronted how determine price...

This study explores the current practices of Design for Deconstruction (DfD) as a strategy achieving circular economy. Keeping in view opportunities accruable from DfD, review literature was carried out and six focus group interviews were conducted to identify key barriers DfD practices. The results phenomenology reveal 26 under five barrier categories. categories are ‘lack stringent legislation policies’, adequate information at design stage’, large enough market recovered components’,...

The emerging technologies of the Internet Things (IoT) and big data can be utilised to derive knowledge support applications for energy-efficient buildings. Effective prediction heating cooling demands is fundamental in building energy management. In this study, a 4-layer IoT-based platform developed day-ahead demands, while core part hybrid machine learning-based predictive model. proposed demand model based on hybrids k-means clustering artificial neural network (ANN). Due different...

The advent of digital technologies has brought substantial improvements in various domains. This article provides a comprehensive review research emphasizing AI-enabled IoT applications poultry health and welfare management. study focused on since modern management is confronted with issues relating to standardized parameters for assessment robust monitoring systems, particularly broilers' disease outbreak prevention. Evidence shown that have high possibilities intelligent automation current...

Abstract Inappropriate management of health and safety (H&S) risk in power infrastructure projects can result occupational accidents equipment damage. Accidents at work have detrimental effects on workers, company, the general public. Despite availability H&S incident data, utilizing them to mitigate accident occurrence effectively is challenging due inherent limitations existing data logging methods. In this study, we used a text‐mining approach for retrieving meaningful terms from...

A genetic algorithm-determined deep feedforward neural network architecture (GA-DFNN) is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom. Due to comprehensive relationship between affecting factors consumption, adoption multiple hidden layers (DFNN) algorithm would improve its prediction accuracy. The DFNN model mainly refers quantity layers, neurons activation function each layer learning process obtain...

The construction industry generates different types of data from the project inception stage to delivery. This comes in various forms and formats which surpass management, integration analysis capabilities existing intelligence tools used within industry. Several tasks lifecycle bear implications for efficient planning delivery projects. Setting up right profit margins its continuous tracking as projects progress are vital management that require data-driven decision support. Existing...

Purpose The purpose of this paper is to highlight the use big data technologies for health and safety risks analytics in power infrastructure domain with large sets risks, which are usually sparse noisy. Design/methodology/approach study focuses on using frameworks designing a robust architecture handling analysing (exploratory predictive analytics) accidents infrastructure. designed based well coherent risk lifecycle. A prototype interfaced various technology artefacts was implemented Java...

This study compared two neural network models (Multilayer Perceptron and Generalized Regression Neural Network) with a view to identifying the best model for predicting students' academic performance based on single factor.Only factor (students' results) was considered as of study.One cohort graduated data collected from Computer Science Engineering Department Obafemi Awolowo University, Nigeria using documents records technique.The were simulated MATLAB version 2015a evaluated mean square...

Purpose In a circular economy, the goal is to keep materials values in economy for as long possible. For construction industry support of there need reuse. However, little or no information about amount and quality reusable obtainable when buildings are deconstructed. The purpose this paper, therefore, develop reusability analytics tool assessing end-of-life status building materials. Design/methodology/approach A review extant literature was carried out identify best approach modelling...

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 biomass are gaining increasing adoption commercial Most previous capacity design approaches formulated based upon conventional operating schedules, which result inappropriate capacities ineffective schedules system. Therefore, a two-stage optimization approach is proposed for system with its optimal...

Concrete is a versatile construction material, but the water content can greatly influence its quality. However, using trials and error method to determine optimum for concrete mix results in poor quality structures, which often end up landfills as wastes, thus threatening environmental safety. This paper develops deep neural networks predict required normal mix. Standard data samples obtained from certified/leading laboratories were fed into learning model (multilayers feedforward network)...

Inaccurate cost estimates have significant impacts on the final of power transmission projects and erode profits. Methods for estimation been investigated thoroughly, but they are not used widely in practice. The purpose this study is to leverage a big data architecture, manage large diverse required predictive analytics. This paper presents analytics modeling system (PAMS) that facilitates use different data-driven prediction methods. A 2.75-million-point dataset has as case study. proposed...

Although advanced machine learning algorithms are predominantly used for predicting outcomes in many fields, their utilisation incident outcome construction safety is still relatively new. This study harnesses Big Data with Deep Learning to develop a robust management system by analysing unstructured datasets consisting of 168,574 data points from power transmission and distribution projects delivered across the UK 2004 2016. compared performance popular (support vector machine, random...