David J Edwards

Predictive maintenance techniques and their relevance to construction plant

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.

A comparative analysis between the multilayer perceptron “neural network” and multiple regression analysis for predicting construction plant maintenance costs

Notes that the real test of maintenance stratagem success (or failure in financial terms) can only be resolved when a comparison of machine maintenance costs can be made to some benchmark standard. Presents a comparative study between two models developed to predict the average hourly maintenance cost of tracked hydraulic excavators operating in the UK opencast mining industry. The models use the conventional statistical technique multiple regression, and artificial neural networks. Performance analysis using mean percentage error, mean absolute percentage error and percentage cost accuracy intervals was conducted. Results reveal that both models performed well, having low mean absolute percentage error values (less than 5 percent) indicating that predictor variables were reliable inputs for modelling average hourly maintenance cost. Overall, the neural network model performed slightly better as it was able to predict up to 95 percent of cost observations to within ≤q £5. Moreover, summary statistical analysis of residual values highlighted that predicted values using the neural network model are less subject to variance than the multiple regression model.

A linear programming decision tool for selecting the optimum excavator

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.

Electronic document management systems and the management of UK construction projects : Investigative research based on literature search and unstructured telephone interviews on electronic document management systems (EDMS) with a range of construction professions

The authors indicate that EDMS will not replace, but complement existing facilities and thereby enhance the management of documentation within construction organizations. It is hoped that client confidence and satisfaction can be enhanced by better communication.

Innovative financing (IF) of infrastructure projects in Ghana: conceptual and empirical observations

Traditional methods of financing have failed to resolve Ghanas infrastructure deficit. Innovative financing (IF) solutions are being encouraged to alleviate this, but presently IF knowledge is limited. This study provides an overview and maps the evolution of IF solutions to conceptually model their characteristics and application to major infrastructure projects, especially in the context of LDCs. An inductive methodology draws extensively on extant literature and published data from Ghanaian ministries, departments and agencies who procure infrastructure works. The study highlights how the IF concept stems from a plethora of public finance issues including reform of government service delivery, new tax tools, public–private partnerships and alternative financing arrangements and further how IF has been focused at educational, road, water infrastructure, housing and district assemblies. Through illumination of the Ghanaian IF concept, the study will be of utility to policy makers and international devel...

The greenhouse effect; Impact upon and the role to be played by construction

The authors highlight several possible ways in which the construction industry could lower present emissions of the greenhouse gas, carbon dioxide. It is an excellent discussion document and is recommended reading.

Modelling culvert construction in artificially frozen ground using finite element analysis

This paper describes the development of a computer finite element method (FEM) model for simulating the temporary earthwork support technique, artificial ground freezing. Specifically, ice‐wall thickness growth and ground movement (due to frost heave and thaw settlement) were evaluated with the use of the finite element software package ABAQUS. Other parameters modelled were obtained from a combination of a priori research and invaluable practitioner experience. Simulation results were then compared with measurements obtained from a live field project to assess model accuracy. Output results obtained from the FEM analyses provided demonstrable evidence of the model’s inherent ability to simulate “realistically” the effects of ground freezing analysis process.