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The purpose of this paper is to identify challenges faced in resource management in the UK construction industry and to propose some solutions to these problems.

Based on a qualitative research methodology, 14 experts from the UK construction industry were chosen to be participants in the study. The participants were equally divided into two focus groups to discuss resource management using five projects as case studies. Thematic analysis of the discussion reveals seven key factors that affect resource management.

The results show that most of the problems identified are due to poor data management processes and the practice of having data in silos. Overcoming this challenge requires the adoption of big data approaches for resource management to allow the integration of large and different forms of data.

This study seeks to bring to the fore challenges faced in resource management by the UK construction industry and to outline some solutions to address them.

In a circular economy, the goal is to keep materials values in the economy for as long as possible. For the construction industry to support the goal of the circular economy, there is the need for materials reuse. However, there is little or no information about the amount and quality of reusable materials obtainable when buildings are deconstructed. The purpose of this paper, therefore, is to develop a reusability analytics tool for assessing end-of-life status of building materials.

A review of the extant literature was carried out to identify the best approach to modelling end-of-life reusability assessment tool. The reliability analysis principle and materials properties were used to develop the predictive mathematical model for assessing building materials performance. The model was tested using the case study of a building design and materials take-off quantities as specified in the bill of quantity of the building design.

The results of analytics show that the quality of the building materials varies with the building component. For example, from the case study, at the 80th year of the building, the qualities of the obtainable concrete from the building are 0.9865, 0.9835, 0.9728 and 0.9799, respectively, from the foundation, first floor, frame and stair components of the building.

As a contribution to the concept of circular economy in the built environment, the tool provides a foundation for estimating the quality of obtainable building materials at the end-of-life based on the life expectancy of the building materials.

The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.

The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.

The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.

This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.

Although the UK Guarantee Scheme for Infrastructures (UKGSI) was introduced in 2012 to address the huge financing gap for critical infrastructures, PFI sponsors have so far guaranteed only few projects. Many stakeholders in the project finance industry have blamed this situation on lack of general understanding of strategies for harnessing the benefits of the government guarantee scheme. The purpose of this paper is to investigate the perspectives of UK’s PFI/PPP stakeholders on critical factors influencing approval for government guarantees using the UKGSI as a focal point.

Using a mixed methodology approach, this study identified 26 important criteria used in evaluating government guarantee applications through focus group discussions with PFI stakeholders. The identified criteria were then put in questionnaire survey to 195 respondents within the UK PFI/PPP industry.

Through factor analysis, five critical factors determining successful government guarantee application were unravelled. These include: compliance with UK National Infrastructure Plan; demonstration of project bankability and risk management; value for money; proof of projects’ dependence on government guarantee; and certainty of planning commission’s approval.

Results of this study will facilitate an in-depth understanding of critical factors necessary for accessing government guarantee scheme for PFI/PPPs, while also improving the bankability of potential PFI projects.

The purpose of this paper is to investigate the performance of natural Jordanian zeolite tuff to remove ammonia from aqueous solutions using a laboratory batch method and fixed-bed column apparatus. Equilibrium data were fitted to Langmuir and Freundlich models.

Column experiments were conducted in packed bed column. The used apparatus consisted of a bench-mounted glass column of 2.5 cm inside diameter and 100 cm height (column volume = 490 cm3). The column was packed with a certain amount of zeolite to give the desired bed height. The feeding solution was supplied from a 30 liter plastic container at the beginning of each experiment and fed to the column down-flow through a glass flow meter having a working range of 10-280ml/min.

Ammonium ion exchange by natural Jordanian zeolite data were fitted by Langmuir and Freundlich isotherms. Continuous sorption of ammonium ions by natural Jordanian zeolite tuff has proven to be effective in decreasing concentrations ranging from 15-50 mg NH₄-N/L down to levels below 1 mg/l. Breakthrough time increased by increasing the bed depth as well as decreasing zeolite particle size, solution flow-rate, initial NH₄+ concentration and pH. Sorption of ammonium by the zeolite under the tested conditions gave the sorption capacity of 28 mg NH₄-N/L at 20°C, and 32 mg NH₄-N/L at 30°C.

This research investigates the performance of natural Jordanian zeolite tuff to remove ammonia from aqueous solutions using a laboratory batch method and fixed-bed column apparatus. The equilibrium data of the sorption of Ammonia were plotted by using the Langmuir and Freundlich isotherms, then the experimental data were compared to the predictions of the above equilibrium isotherm models. It is clear that the NH₄+ ion exchange data fitted better with Langmuir isotherm than with Freundlich model and gave an adequate correlation coefficient value.

This study aims to provide an excellent overview of current research trends in the construction sector in digital advancements. It provides a roadmap to policymakers for the effective utilisation of emergent digital technologies and a need for a managerial shift for its smooth adoption.

A total of 3,046 peer-reviewed journal review articles covering Internet of Things (IoT), blockchain, building information modelling (BIM) and digital technologies within the construction sector were reviewed using scientometric mapping and weighted mind-map analysis techniques.

Prominent research clusters identified were: practice-factor-strategy, system, sustainability, BIM and construction worker safety. Leading countries, authors, institutions and their collaborative networks were identified with the UK, the USA, China and Australia leading this field of research. A conceptual framework for an IoT-based concrete lifecycle quality control system is provided.

The study traces the origins of the initial application of Industry 4.0 concepts in the construction field and reviews available literature from 1983 to 2021. It raises awareness of the latest developments and potential landscape realignment of the construction industry through digital technologies conceptual framework for an IoT-based concrete lifecycle quality control system is provided.

This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO₂ prediction models. The purpose of this paper is to pre-process a relatively large data of NO₂ from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO₂ prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.

The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO₂ pollution.

This paper's hybrid model provides a basis for giving an informed decision on the NO₂ pollutant avoidance system.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration.

Automation facilitates production activities within offsite construction (OSC) projects through computer-controlled and mechanised systems that can be programmed to deliver various products in a self-regulating sequence. Despite known benefits of automation to offsite production, the level of automation adoption in New Zealand is low. This study is an effort to understand the current status of automation within the New Zealand construction industry and to identify the barriers and enablers to its uptake.

This study utilises the qualitative approach of semi-structured interviews (open-ended questions). Using a referral sampling strategy (snowballing), fifteen New Zealand industry experts were interviewed, and the data collected were analysed using qualitative content analysis.

The study found that there is a weak business case for full automation. Four main categories of barriers to the uptake of automated OSC were identified, including requirement of high capital cost, lack of education about automation and OSC and non-existence of regulations to support OSC. It was noted that financial supports to the OSC sub-sector in form of subsidies, tax waivers, and enhanced leasing model could enhance the uptake of automation. Further to this more awareness about OSC's automation and regulations suitable for OSC could enhance the confidence of business owners to invest in this area.

Originality of this paper stems from the fact that, not much attention has been paid to investigating the uptake of automation for OSC sub-sector of construction industry in New Zealand context.

The purpose of this paper is to expand the benefits of building information modeling (BIM) to include data analytics to analyze construction project performance. BIM is a great tool which improves communication and information flow between construction project parties. This research aims to integrate different types of data within the BIM environment, then, to perform descriptive data analytics. Data analytics helps in identifying hidden patterns and detecting relationships between different attributes in the database.

This research is considered to be an inductive research that starts with an observation of integrating BIM and descriptive data analytics. Thus, the project’s correspondence, daily progress reports and inspection requests are integrated within the project 5D BIM model. Subsequently, data mining comprising association analysis, clustering and trend analysis is performed. The research hypothesis is that descriptive data analytics and BIM have a great leverage to analyze construction project performance. Finally, a case study for a construction project is carried out to test the research hypothesis.

The research finds that integrating BIM and descriptive data analytics helps in improving project communication performance, in terms of integrating project data in a structured format, efficiently retrieving useful information from project raw data and visualizing analytics results within the BIM environment.

The research develops a dynamic model that helps in detecting hidden patterns and different progress attributes from construction project raw data.