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This paper aims to present an automated system that measures the volume of excavated soil in earthmoving operations. It focuses primarily on presenting the use of the radio frequency identification – real time location technology and image analysis techniques in isolation of stockpiles from their noisy backgrounds, extraction of their geometrical attributes and determination of their location.

An extensive literature review of image analysis techniques is performed to identify the ones most applicable in developing the proposed automated system. A set of techniques are selected and experimented to evaluate their effectiveness in the proposed application. A review of the state‐of‐the‐art distance measurement technologies is also conducted to identify the most suitable system to be used in the proposed system.

A set of image analysis techniques that positively contribute to the accuracy of the proposed system is identified. Geometrical attributes of stockpiles are automatically extracted for volume measurement purposes. The use of an affordable and reliable automated distance measurement tool is also suggested.

Although the proposed methodology is believed to be applicable to most configurations of stockpiles, it was tested on conical ones only.

The current practice of measuring the volume of excavated soil is time consuming and costly. Furthermore, it does not facilitate monitoring of excavation activities on close time intervals. The proposed system overcomes the problems associated with the current practice and provides an automated strategy that can be easily used by field personnel and/or home office management staff to closely monitor the progress of their earthmoving operations without physical human intervention.

Determining the duration for road construction projects represents a problem for construction professionals in Ghana. The purpose of this paper is to develop an artificial neural network (ANN) model for determining the duration for rural bituminous surfaced road projects.

Data for 22 completed bituminous surfaced road projects from the Department of Feeder Roads (rural road agency) were collected and analyzed using the principal component analysis (PCA) and ANN techniques. The data collected were final payment certificates which contained payment bill of quantities (BOQ) of work items executed for the selected completed road projects. The executed quantities in the BOQ were the total quantities of work items for site clearance, earthworks, in-situ concrete, reinforcement, formwork, gravel sub-base/base, bitumen, road line markings and furniture, length of road and actual durations for each of the completed projects. The PCA was first employed to reduce the data in order to identify a smaller number of variables (or significant quantities) that constitute 81.58 percent of the total variance of the collected data. The ANN was then used to develop the network using the identified significant quantities as input variables and the actual durations as output variables.

The coefficient of correlation (R) and determination (R²) as well as the mean absolute percentage error (MAPE) obtained show that construction professionals can use the developed ANN model for determining duration. The study shows that the best neural network is the multi-layer perceptron with a structure 3-38-1 based on a back propagation feed forward algorithm. The developed network produces good results with an MAPE of 17.56 percent or an average accuracy of 82.44 percent.

Apart from the fact that the sample size was small, the developed model does not incorporate the implications of other likely factors that may affect contract duration.

The outcome of this study is to help construction professionals to fix realistic contract duration for road construction projects before signing a contract. Such realistic contract duration would help reduce time overruns as well as the payment of liquidated and ascertained damages by contractors for late completion.

This paper proposes an alternative way of determining the duration for road construction projects using the total quantities of work items in a final payment BOQ. The approach is based on the PCA and ANN model of quantities of work items of completed road projects.

Climate adaptation, energy efficiency, sustainable development and green growth are societal challenges for which the Facilities Management (FM) profession can develop solutions and make positive contributions on the organisational level and with societal-level effects. To base the emerging sub-discipline of sustainable facilities management (SFM) on research, an overview of current studies is needed. The purpose of this literature review is to provide exactly this overview.

This article identifies and examines current research studies on SFM through a comprehensive and systematic literature review. The literature review included screening of 85 identified scientific journals and almost 20,000 articles from the period of 2007-2012. Of the articles reviewed, 151 were identified as key articles and categorised according to topic.

The literature review indicated that the current research varies in focus, methodology and application of theory, and it was concluded that the current research primary addresses environmental sustainability, whereas the current research which takes an integrated strategic approach to SFM is limited. The article includes lists of reviewed journals and articles to support the further development of SFM in research and practice.

The literature review includes literature from 2007 to 2012, to manage the analytical process within the project period. However, with the current categorisation and the access to the reviewed journals and articles, it is possible to continue with the latest literature.

The article provides an overview of theoretical and practical knowledge which can guide: how to document and measure the performance of building operations in terms of environmental, social and economical impacts? How to improve the sustainability performance of buildings? What are the potentials for and barriers to integrating sustainability into FM on strategic, tactical and operational levels?

The paper presents the most comprehensive literature study on SFM so far, and represents an important knowledge basis which is likely to become a key reference point for pioneers and scholars in the emerging sub-discipline of SFM.

Building information modelling (BIM) is increasingly being adopted during construction projects. Design and construction practices are adjusting to the new system. BIM is intended to support the entire project life-cycle: the design and construction phases, and also facility management (FM). However, BIM-enabled FM remains in its infancy and has not yet reached its full potential. The purpose of this paper is to identify major aspects of BIM in order to derive a fully BIM-enabled FM process.

In total, 207 papers were classified into main and subordinate research areas for quantitative analysis. These findings were then used to conceptualise a BIM-enabled FM framework grounded by innovation diffusion theory for adoption, and for determining the path of future research.

Through an extensive literature review, the paper summarises many benefits and challenges. Major aspects of BIM are identified in order to describe a BIM-enabled FM implementation process grounded by innovation diffusion theory. The major research areas of the proposed framework include: planning and guidelines; value realisation; internal leadership and knowledge; procurement; FM; specific application areas; data capture techniques; data integration; knowledge management; and legal and policy impact. Each element is detailed and is supported by literature. Finally, gaps are highlighted for investigation in future research.

This paper systematically classifies and evaluates the existing research, thus contributing to the achievement of the ultimate vision of BIM-enabled FM. The proposed framework informs facility managers, and the BIM-enabled FM implementation process. Further, the holistic survey identifies gaps in the body of knowledge, revealing avenues for future research.

The purpose of this paper is to present an artificial intelligent (AI) system for estimating the construction cost of water and sewer rehabilitation projects.

To develop the proposed system, data pertaining to 54 sewer and water rehabilitation projects was collected. The collected data were analyzed using Pareto analysis technique to identify the most important factors that contribute positively to the cost estimation process. These factors were then utilized to develop a neural network (NN) model that estimates the construction cost of this class of projects.

The study reveals a set of 23 factors that highly impact the construction cost of water and sewer network rehabilitation projects and presents a NN model that predicts the cost of these projects with high accuracy.

The proposed system was developed using information obtained from the city of San Diego, California, USA. The cost of these projects ranged from $800,000 to $7 million. The diameter of pipes installed in these projects ranged from 1 in. to 36 in. and their length was up to about 2.7 miles.

The developed system saves time, improves the accuracy of the estimates and prevents problems that are usually associated with inaccurate estimates. The system will not only help funding authorities to ensure maximum utilization of resources, but will also help cities to manage their expenditures in a manner that assures satisfactory performance of their buried assets. Furthermore, the developed system is also believed to assist cities in comparing alternatives and the go/no‐go decision making process.

This paper aims to investigate the relationship between building information modelling (BIM) adoption from the perspectives of people, process and technology to strategic information technology (IT) in construction mediated by collaborative processes for new BIM entrants. The demand pull for more effective project delivery in the construction industry across the globe has continued to transform design techniques from two-dimensional, three-dimensional (3D) and, currently, BIM. Leverage on IT is pivotal for construction industry development as earmarked by the Malaysian construction industry master plan. BIM uptake by stakeholders in Malaysia construction industry is on a gradual increase. BIM generates and manages building data during its life cycle via 3D, real-time, dynamic building modelling.

BIM model is dependent on collaborative contribution by project teams’ input at various stages. However, challenges such as people, process and technology impede an effective adoption rate in Malaysia. This paper presents a continuation of an ongoing theoretical framework developed, to further investigate the relationship between BIM adoption from perspectives of people, process and technology to strategic IT in construction and collaborative process. To develop the framework, an extensive literature review on factors affecting BIM adoption was carried out. The underlining gap stems from a positivist standpoint in examining human – IT interaction. The model builds on conceptual technology acceptance models incorporating strategic IT implementation and collaborative processes in the industry.

The paper examined factors affecting BIM adoption in Malaysia. The factors are further affected by the environment where BIM is utilised. The paper also presents the mediating effect of collaboration for new BIM entrants. Overall, the theorised hypotheses delineate the strength and significance of the examined relationship within the model. Subsequently, the data will be collected from construction industry professionals (architects, quantity surveyors, engineers and contractors) through the developed survey instrument. The hypotheses will undergo structural equation modelling to analyse the desired statistical power, test for close versus exact fit and complexity of the model.

The findings will delineate the variables with predominant impact on BIM adoption and serves as a guide to future policymaking on BIM implementation in Malaysia.

Current research on BIM in Malaysia is limited to readiness and awareness. This paper extends the need for empirical findings from construction professionals’ perception of BIM. The findings also explain reasons for BIM adoption in new BIM entrants and add to current body of knowledge on IT acceptance model formation.

A major concern for construction professionals at the rural road agency in Ghana is the problem of fixing contract duration for bridge construction projects in rural areas. The purpose of the study was to develop a tool for construction professionals to forecast duration for bridge projects.

In all, 100 questionnaires were distributed to professionals at the Department of Feeder Roads to ascertain their views on the work items in a bill of quantities (BOQ) that impact significantly on the duration of bridge construction projects. Historical data for 30 completed bridge projects were also collected from the same Department. The data collected were executed work items in BOQ and actual durations used in completing the works. The qualitative data were analysed using the relative importance index and the quantitative data, processed and analysed using both the stepwise regression method and artificial neural network (ANN) technique.

The identified predictors, namely, in-situ concrete, weight of prefabricated steel components, gravel sub-base and haulage of aggregates, used as independent variables resulted in the development of a regression model with a mean absolute percentage error (MAPE) of 25 per cent and an ANN model with a feed forward back propagation algorithm with an MAPE of 26 per cent at the validation stage. The study has shown that both regression and ANN models are appropriate for predicting the duration of a new bridge construction project.

The predictors used in the developed models are limited to work items in BOQs only of completed bridge construction projects as well as the small sample size.

The study has developed a working tool for practitioners at the agency to forecast contract duration for bridge projects prior to its commencement.

The study has quantified the relationship between the work items in BOQs and the duration of bridge construction projects using the stepwise regression method and the ANN techniques.

Selecting the most appropriate architecture‐engineering (AE) team, one of the most significant decisions leading to the successful completion of a construction project, is usually conducted in a multi‐criteria environment, which is mostly dependent on the subjective judgment of decision makers and is influenced by the uncertainty and vagueness of each individual construction project. This paper aims to present an assessment method to evaluate the capability of an AE team with respect to the criteria defined by decision makers.

In addition to a proposed tender price, the evaluation of potential AE teams should be also based on other criteria such as its financial soundness, experience, expertise, availability, and compatibility of personality. A selection model is developed, in which different decision criteria and its subcriteria, and their combinations are simultaneously taken into account by using the concept of fuzzy set theory. An illustrative example is also provided in the paper to demonstrate the application of the model and its assessment method in selecting the most appropriate AE team for a construction project.

The study results show that the proposed evaluation model allows decision makers to express their opinions about the performances of AE bidders using more realistic qualitative and linguistic terms and the fuzzy decision making method is an appropriate tool that can assist decision makers to better evaluate AE bidders' qualifications and select the best firm so that the risk to the project failure is minimized.

While the proposed method is a useful tool for selection of the most appropriate AE team for construction projects, it should be simply used as a guide in reviewing the qualifications of different AE candidate firms and the final decision should be made in accordance with the ultimate goal of the project owner.

The model is proven suitable for quantifying imprecise information, reasoning, and making decisions based on vague and incomplete data.

Improvements in quality have a very important role to play in services because such improvements add greater flexibility and give clients greater confidence in the services provided. Therefore, this paper aims to describe a framework for measuring quality in the provision of the service by using a SERVQUAL scale and fuzzy operators.

In this approach, a framework to assess quality in service provision is put forward which applies the SERVQUAL scale as an instrument of data collection. Fuzzy set theory is proposed and applied within this framework as a technique to make a quantitative assessment of the quality of the provision of services. A case study is used to illustrate how to apply this framework for this purpose. The data were processed and transformed into a fuzzy environment, and fuzzy numbers and operators were applied to the analysis of customers' assessment with regard to the dimensions of quality in the service provided.

The study demonstrated the differences between evaluations of the dimensions of quality assessed, and differences of the same customer in relation to these dimensions. The main points raised were the importance given by customers to the dimensions, and the gaps between customers' perceptions and expectations, when fuzzy numbers were used to assess levels of service quality, and to evaluating the prioritization of service quality.

This paper proposes the use of fuzzy theory within a framework by making a linguistic analysis when dealing with data collected in a SERVQUAL scale so as to assess service quality. The data were measured by examining the gap between customers' perceptions and expectations. The framework describes the phases of this assessment, and uses fuzzy operators.