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The heavy civil engineering industry (railways, sewage treatment, chemical and pharmaceutical facilities, oil and gas facilities, etc.) is one of the major contributors to the British economy and generally involves a high level of investment. Clients in this industry are demanding accurate cost estimate, proper analysis of out‐turn cost and cost escalation and a high quality risk analysis throughout the construction processes. Current practice in the industry has suggested that there is a lack of structured methodologies and systematic cost escalation approach to achieve an appropriate cost analysis at the outset of projects and throughout the construction processes. In this context the prime objective of this research work is to develop a structured cost escalation methodology for improving estimating management and control in the heavy engineering industry construction processes. The methodology is composed of a forecasting model to predict cost indices of major items in industry and a risk knowledge base model for identifying and quantifying causes of cost escalations. This paper, as part of the research, reviews and discusses a knowledge‐based model for applying a cost escalation factor. The cost escalation factor is made up of market variation, a risk element and a component for bias. A knowledge elicitation strategy was employed to obtain the required knowledge for the model. The strategy included questionnaires, interviews and workshops and deliverables came in the form of influences and their effect on project cost escalation. From these deliverables, the concepts of a decision support model and system specification for applying cost escalation to base estimates is proposed.

The purpose of this research is to develop through a two-stage verification and validation process a novel implementation framework for collaborative BIM, utilising experts from academia and industry as well as a real-world case study project.

The aim of this research was to build upon previous research findings by the authors in order to develop an implementation framework that stems from ousting the inefficiencies of current collaborative BIM practices. This is achieved by a more objectified and quantified approach towards seeking heightened transparency and objectivism of what is required through the implementation of BIM. The mixed research methods technique of both qualitative and quantitative data collection was utilised, with the structure consisting of a two-stage approach utilising the Delphi model for verification and validation. This was developed to test the novelty and beneficial structure hypothesis involving 15 core BIM experts from academia, construction and design with c. 22 years average experience. Validation was undertaken on a complex, high value real world building structures project in central London, inclusive of 8 core project BIM experts. The research utilised a developed solution that mirrored and provided a more holistic representation guiding the practitioners as a project team step by step through the determination of underpinning elements, which support the goal of enhanced information requirements as well as executing the prioritisation measurement tools as part of the framework. Data ascertained at the workshop case study prioritised areas of importance that are core in supporting the delivering of these enhanced information requirements at a project delivery level, which were in order of prioritisation determined by the project team (1) constraints (39.17%), (2) stakeholder requirements (35.78%), (3) coordination (existing asset) (15.86%), (4) exchange requirements (5.38%) and (5) level of information need (3.81%). Furthermore, risk mitigations for the top three priorities were focussed on early stakeholder engagement, appropriation of survey data collection, focus on quality of outputs and applying toolsets and processes with meaning and emphasis on the defined high-level requirements.

Findings show that the framework and the developed solution translate the process methodology of the framework schema into a useable and beneficial tool that provides both qualitative and quantitative inputs and outputs. Furthermore, a collective agreement on the objectives, risk mitigations and assignment of tasks in order to achieve outcomes is presented, with evidence on numerical weightings and goal achievement.

Due to the impacts of COVID-19 on physical engagements both the verification (electronic survey questionnaire) and validation (case study project) were undertaken remotely, using available technologies and web interfaces.

The case study workshop was limited to one building structures project in central London of a value of c. £70 m design and build cost that the project team (participants) were actively engaged with.

The social impacts of this research has resulted in the review of existing systems, methods and approaches from a wider perspective of theoretical and applied environments, which led to the development of a novel approach and framework guided by an interactive and useable solution.

As shown within the core findings, experts across academia and industry (design and construction) confirmed that the framework methodology and application were 100% novel, and added a benefit to the existing collaborative BIM approach. Value added is that through objectifying, weighting/prioritizing and creating a discussion supported by qualitative and quantitative reasoning the focus on what collaborative BIM is to achieve is increased, and thus the likelihood of successful implementation.

Energy analysis (EA) within a building information modelling (BIM) enables consistent data integration in central repositories and eases information exchange, reducing rework. However, data loss during information exchange from different BIM uses or disciplines is frequent. Therefore, a holistic approach for different BIM uses enables a coherent life cycle information flow. The life cycle information flow drives the reduction of data loss and model rework and enhances the seamless reuse of information. The latter requires a specification of the EA key performance indicators (KPIs) and integrating those in the process.

The paper presents a set of KPIs extracted from the developed EA process maps and interviews with expert stakeholders. These KPIs stem from the literature review and link to the benefits of EA through industry expert review. The study includes (1) development and validation of EA process maps adjusted to requirements from different stakeholders. (2) KPIs aligned with the EA process map, (3) identification of the drivers that can facilitate life cycle information exchange and (4) opportunities and obstacles for EA within BIM-enabled projects.

This paper depicts a viable alternative for EA process maps and KPIs in a BIM-enabled AEC design industry. The findings of this paper showcase the need for an EA within BIM with these KPIs integrated for a more effective process conforming to the current Open BIM Alliance guidance and contributing towards sustainable life cycle information flow.

The limitation of the research is the challenge of generalising the developed EA process maps; however, it can be adjusted to fit defined organisational use. The findings deduced from the developed EA process map only show KPIs to have the ability to facilitate adequate information flow during EA.

The AEC industry will benefit from the findings of this primary research as the industry will be able to contrast its process maps and KPIs to those developed in the paper.

This paper benefits the societal values in EA for the built environment in the design stages. The subsequent life cycle information flow will help achieve a consistent information set and decarbonised built environment.

The paper offers a practical overview of process maps and KPIs to embed EA into BIM, reducing the information loss and rework needed in the practice of this integration. The applicability of the solution is contrasted by consultation with experts and literature.

The monitoring and control of business processes and their variables have strategic importance in order to respond to the dynamics of the world of business. Many monitoring processes are focussed on controlling time and cost and the overall performance is evaluated through a standard set of key performance indicators. These passive approaches do not consider a holistic/system view and therefore ignore the interrelationships between various external and internal variables impacting a business process. This paper investigates an application of multivariate statistical process control techniques [mainly principal component analysis (PCA) and partial least squares (PLS)] which have been successfully used in process and chemical industries, to model, monitor, control and predict business process variables. A prototype, innovative managerial control system (IMCS), was developed to investigate the application of PCA and PLS techniques to monitor, control and predict business process performance. Data was collected and analysed using a case study in a precast concrete building products company. This study has proved that the PCA approach can be effectively used to control business processes. Also, the PLS approach is found to provide better forecasts as compared to commonly used decomposition method. The benefits and limitations of using multivariate statistical process control techniques as applied to business process control are highlighted.

The risk factors associated with clients have a major impact on the successful delivery of a project from early design to the construction and operation stages. Risk management studies conducted so far have not succeeded in providing an effective risk assessment methodology for clients in analysing and managing the risk factors that cause both project delays and cost overruns. So, the purpose of this paper is to provide a methodology for a client-based risk management model.

A conceptual framework is designed by integrating the findings from a literature review and a construction industry survey in the Kingdom of Saudi Arabia. The framework includes the risk identification, risk analysis and mitigation strategy, which are the key components of the model. The model of the framework is developed by integrating the analytical hierarchy process (AHP) and Monte Carlo simulation (MCS) underpinned within an @Risk program.

A case study is used to demonstrate the proposed methodology; the results found that the model helps to analyse and quantify the impact of risk factors, and also to assist in taking a suitable risk mitigation strategy, particularly at the early design stage in the construction process.

The model is applicable to both public and private clients when they need to know the possible project duration in a new construction project, and to take some proactive actions to avoid the adverse effect of client risk factors at the early stage of the project.

The model is expected to help in understanding the nature, and analysing the influence, of client risk factors that cause project delays and cost overruns. The development of the methodology for managing the client-based risk in construction processes at the early design stage is the key value of the study.

The current uptake of 4D planning in industry is slow and there is a need to demonstrate its value over traditional planning technologies. The aim of this research study is to develop a novel approach to establish the value of a 4D tool in the construction industry.

The exploratory research strategy draws on several social science research methods to collect information from human subjects. This exploratory research work has used literature review, open‐ended questionnaire, surveys, semi‐structured interviews and historical site records. These have been analysed in order to identify, develop and quantify 4D‐based key performance indicators.

The paper identifies and quantifies 4D‐based key performance indicators. Analysis of the planning efficiency (hit rate percentages) measure on three projects shows that, on an average, a 17 per cent increase in the average industry hit rate was achieved by the use of 4D technology. Also the quantification of communication efficiency measure has shown that on average 30 per cent of meeting time was saved by the use of 4D planning.

The complexity and rapidly paced development of today's projects are challenging the industry to find new innovative approaches to deliver projects. 4D is emerging as a construction‐planning technology to address some of these challenges. 4D planning has the potential to improve visualisation of building design and construction, but its implementation in the industry has yet to reach maturity. This technology enables clients, contractors, planners and sub‐contractors to visualise and understand design and scheduling issues at the early stages of the project.

Despite its benefits, the uptake of 4D planning in the construction industry is slow and therefore there is a need to demonstrate its value over traditional planning technologies. The aim of this paper is to develop a novel approach that demonstrates the value of 4D tools to the construction industry.

The research strategy utilised draws on several social science research methods. The data collection methods employed included a literature review, an open‐ended questionnaire, surveys, semi‐structured interviews and the analysis of historical site records. The data collected were analysed using qualitative and quantitative techniques in order to identify, develop and quantify 4D‐based key performance indicators.

This paper identifies and quantifies 4D‐based key performance indicators using case study analysis. In the case studies it was found that, on average, a 17 per cent increase in planning efficiency were achieved by the use of 4D technology, while the communication efficiency measure illustrated that, on average, a 30 per cent reduction in the time used for meetings was achieved by the use of 4D planning.

The complexity and rapid pace of development in today's construction projects are challenging the industry to find new innovative approaches to delivering projects. 4D tools are emerging as a construction planning technology that addresses some of these challenges. 4D planning has the potential to improve the visualisation of building design and construction, but its implementation in the industry has yet to reach maturity.

The paper highlights technology that enables clients, contractors, planners and sub‐contractors to visualise and understand design and scheduling issues at the early stages of a project.

The concrete building products manufacturing industry supplies 2,000‐4,000 precast concrete building products to the construction industry. Owing to seasonal demand, the industry builds up stock in winter to meet the high demand in summer. As concrete products are heavy and vary in shape and size, proper stocking in terms of layout and methods of stocking of products on the yard is essential. Industrial practice suggests that stockyard space management gets less attention during strategic and budget planning as it is left to the stockyard manager. The industry experiences space congestion for both the storage and dispatch of products. During dispatch process, greater retrieval time is required, long queues of lorries (shipping vehicles) are formed and desired level of service cannot be maintained. Presents a review of stockyard operations, analysis of parameters affecting loading and dispatch process on the yard and strategies to optimise the stockyard layout. It is expected that proper layout planning will reduce the cost of delivery of products by 5‐10 per cent in the industry where profit is less than 5‐8 per cent.