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The purpose of this paper is to investigate the outcomes of Indonesian power projects as representative projects of Asian emerging economies that were procured via public-private partnerships (PPPs) and traditional public sector procurement. Power generation infrastructure delivery in emerging economies frequently seeks private participation via PPPs as one of the key mechanisms to attract private finance. Undertaking a comparative benchmark study of the outcomes of Indonesian power projects provides an opportunity to explore the historic evidence as to whether PPPs deliver better outcomes than traditional public procurement in emerging economies.

This paper reports on a study of the performance of 56 Indonesian power projects procured via either PPPs or traditional procurement. First, it focusses on project time and cost outcomes of power plant facility during construction and commissioning and then extends this comparison to consider the operating availability of power plants during their first two years of operation.

The results indicate that PPP projects had superior time and operating availability to those procured traditionally whereas no significant differences were identified in the cost performance between PPPs and traditionally procured projects. These findings highlight the importance of adopting policies that are supported by broader sources of international financiers and high quality power plant developers.

The quality performance analyses of projects (based on equivalent available factor indices) were limited to the power plants in the Java-Bali region where the majority of projects are large scale power plants.

This study provides an empirical basis for governments of emerging economies to select the most beneficial procurement strategy for power plant projects. It highlights the importance of selecting experienced providers and to adopt policies that attract high quality international project financiers and power plant developers. This includes the need to ensure the commercial viability of projects and to seriously consider the use of cleaner power technologies.

This study is the first to compare the outcomes of power projects in Asian emerging economies delivered via PPPs against those delivered by traditional public procurement that includes consideration of the quality of the delivered product.

The construction industry relies increasingly on profits generated from high utilisation of mechanisation. Interruption of this mechanical supply not only incurs the “tangible” costs of labour, replacement parts and consumables, but also the less tangible costs of delays to contract, possible loss of client goodwill and ultimately, loss of profit. Cumulative costs associated with plant breakdown are therefore significant. Predictive maintenance (PM) techniques have evolved to keep a check on mechanical health, by generating information on machine condition. Such data allow just in time maintenance to be conducted. However, recent developments have witnessed an increased interest in determining “root cause” of failure as opposed to monitoring the time to breakdown once the wear process has begun. This paper reviews condition based monitoring (CBM) technologies and introduces the evolving concept of root cause analysis. Both these could have particular relevance to construction plant and equipment. In summary, the paper presents initial findings of ongoing research, which is the development of a model for predicting construction plant and equipment breakdown.

The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.

Hiring, selecting or assessing plant operatives' proficiency in the UK construction industry is an increasingly difficult task. A number of plant operator certification schemes are available to practitioners and each scheme trains to a myriad of bespoke standards. Consequently, the decision to employ a candidate often rests upon the employer's intuition and judgement and creates an unnecessary dilemma. To address this aforementioned problem, findings of research work that modelled plant operators' maintenance proficiency is presented. A UK nationwide survey was conducted to elicit plant professional opinion on what ‘training and educational’ (T&E) attributes constitute ‘good’ operator proficiency. The data was then arranged into three categories of operator maintenance proficiency: good, average and poor Multivariate Discriminant Analysis (MDA) was used on 75 percent of a simulated data set. The model utilised five T&E attributes, namely: duration of training provided, operator holder of alternative training card (not Certificate of Training Achievement (CTA) or Scottish/National Vocational Qualifications (S/NVQ)), operator's oral communication skills, operator's planning skills and operator's mechanical knowledge. Performance analysis revealed that model classification accuracy was 89.10 percent. The remaining 25 percent hold out sample was then modelled for validation purposes using the derived MDA model. Accuracy of the sub‐sample model was high at 77.60 percent whilst a paired sample T‐tests for the 75 percent and 25 percent sample data established that there was no significant statistical difference between actual and predicted classifications. Future work is proposed that aims to model other factors that influence operator maintenance proficiency; namely, work situational, motivational management and personal factors.

The construction industry suffers from fragmentation owing to the temporary nature of project execution and the specialisms incorporated into a project. Major construction industry clients, contractors and consultants were interviewed or sent questionnaires to elicit their views on performance on large contemporary construction projects and the problems experienced by civil engineering contractors during construction. Management should mitigate the above effects by adopting a project strategy that combines individual and group skills to have the best balance of resources available at the right time. The trend of major clients towards executive project management should be encouraged, since only they can view a project in its entirety.

The main objective of the study is to present a systematic process that can assess, compare and benchmark different geographical levels environmental impacts of using sustainable materials at construction stage.

Current study presents a methodological framework to evaluate environmental impacts at the construction stage of using sustainable materials through a cradle-to-gate process based quantitative LCA study. Scenario analyses and an optimisation analysis using Monte-Carlo simulation are conducted to investigate the influence of external factors on environmental impacts at different geographical regions.

Materials account for 98% of greenhouse gas (GHG) emissions. Carbon monoxide (CO) and non-methane volatile organic compounds (NMVOC) record significant non-GHG emissions. Particulate matter (PM₁₀) emissions are significant from transportation and equipment. High significance of global warming potential (GWP) (38.98%) and photochemical oxidation formation potential (POFP) (34.49%) at global level and eutrophication potential (EP) (52.83%) and human toxicity potential (HTP) (25.30%) impacts at local level were observed. Shortest transportation distance reduces 14.91% PM₁₀ and 4.69% nitrous oxide (NO_x) emissions. Inventory variations have major influence on POFP impact at global level. Local level impacts are not significantly affected by inventory variations. Optimisation analysis indicated, high fly ash in concrete increase local level carbon emissions, if OPC concrete transportation distance is less than 23.7 km.

Use of case-specific information for validation may lack generalisation. However, methodology can be used for future sustainable decision making over using sustainable materials in construction.

The study estimate environmental impacts at different geographical levels when sustainable materials are used for construction.

Effective management and utilisation of plant history data can considerably improve plant and equipment performance. This rationale underpins statistical and mathematical models for exploiting plant management data more efficiently, but industry has been slow to adopt these models. Reasons proffered for this include: a perception of models being too complex and time consuming; and an inability of their being able to account for dynamism inherent within data sets. To help address this situation, this research developed and tested a web‐based data capture and information management system. Specifically, the system represents integration of a web‐enabled relational database management system (RDBMS) with a model base management system (MBMS). The RDBMS captures historical data from geographically dispersed plant sites, while the MBMS hosts a set of (Autoregressive Integrated Moving Average – ARIMA) time series models to predict plant breakdown. Using a sample of plant history file data, the system and ARIMA predictive capacity were tested. As a measure of model error, the Mean Absolute Deviation (MAD) ranged between 5.34 and 11.07 per cent for the plant items used in the test. The Root Mean Square Error (RMSE) values also showed similar trends, with the prediction model yielding the highest value of 29.79 per cent. The paper concludes with direction for future work, which includes refining the Graphical User Interface (GUI) and developing a Knowledge Based Management System (KBMS) to interface with the RDBMS.

This paper aims to consider opposing influences on workplace plant and machinery health and safety (PMH&S) innovations, highlight examples of these to model PMH&S innovations’ effectiveness at the workplace, develop guidance for improvement of same and for construction of health and safety (H&S) performance.

This study is a qualitative meta-analysis of data distributed among a sample of published research in the field, and it uses inductive reasoning based on informal, qualitative and interpretative analysis.

Nearly all PMH&S innovations (positive influences) originate from original equipment manufacturers and specialist companies throughout the supply chain. Negative influences that can counter these potential H&S benefits result mainly from human (in) action(s) at the workplace. These are classified (and analysed) in terms of “error”, “indifference” and “lack of training”. “Tolerant” H&S management is another negative influence found among these classifications.

The study draws from a targeted meta-sample of research in the field, a model of positive and negative influences on PMH&S innovations that emphasises workers’ (in) action(s).

Previous methods have been developed to predict tracked hydraulic excavator output and associated costs of production, but these fail to provide a “complete” solution to the plant productivity problem. That is, when hiring or purchasing machines plant managers are not normally provided with sufficient detail to optimise the plant selection decision process. The crux of this problem is to choose an appropriate plant item from the vast range available. This paper contributes to resolving this selection process through the application of an optimisation technique, based on linear programming. Specifically, a decision tool for selecting the optimum excavator type for given production scenarios is presented. In achieving this aim, a mass excavation task was specified as the principal decision criterion. Production output and machine hire costs were predicted using both multivariate and bivariate regression models. The decision tool performed well during testing and therefore exhibits significant potential for use by practitioners. The paper concludes with direction for future research work; concentrating on development of a software package for accurately predicting productivity rates and assisting in the plant selection process.

Lead, one of the oldest corrosion‐resistant materials known to chemical engineers, still plays a prominent part in plant construction. This article outlines its special features and applications in chemical plant, and discusses some of the special techniques that have been developed to meet industry's changing needs.