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Tracked hydraulic excavators are versatile and ubiquitous items of off-highway plant and machinery that are utilised throughout the construction industry. Each year, a significant number of excavators overturn whilst conducting a lifting operation, causing damage to property, personnel injury or even fatality. The reasons for the overturn are myriad, including: operational or environmental conditions; machine operator acts or omissions; and/or inadequate site supervision. Furthermore, the safe working load (SWL) figure obtained from manufacturer guidance and utilised in lift plans is based upon undertaking a static load only. The purpose of this paper is to determine whether the SWL is still safe to be used in a lift plan when slewing a freely suspended (dynamic) load, and, if not, whether this may be a further contributory factor to overturn incidents.

Previous research has developed a number of machine stability test regimes but these were largely subjective, impractical to replicate and failed to accurately measure the “dynamic” horizontal centrifugal force resulting from slewing the load. This research contributes towards resolving the stability problem by critically evaluating existing governing standards and legislation, investigating case studies of excavator overturn and simulating the dynamic effects of an excavator when slewing a freely suspended load at high rotations per minute (rpm). To achieve this, both the static load and horizontal centrifugal force from slewing this load were calculated for six randomly selected cases of an excavator, with different arm geometry configurations.

The results from the six cases are presented and a worked example of one is detailed to demonstrate how the results were derived. The findings reveal that the SWL quoted on an excavator’s lift rating chart considerably underestimates the extra forces experienced by the machine when an additional dynamic load is added to the static load whilst lifting and slewing a freely suspended load.

This work presents the first attempt to accurately model excavator stability by taking consideration of the dynamic forces caused by slewing a freely suspended load and will lead to changes in the way that industry develops and manages lift plans. Future research proposes to vary the weight of load, arm geometry and rpm to predict machine stability characteristics under various operational conditions, and exploit these modelling data to populate pre-programmed sensor-based technology to monitor stability in real time and automatically restrict lift mode operations.

Utilization of off‐highway vehicles forms an essential part of UK industry's efforts to augment the productivity of plant operations and reduce production costs. However, uninterrupted utilization of plant and equipment is requisite to reaping the maximum benefit of mechanization; one particular problem being plant breakdown duration and its impact upon process productivity. Predicting the duration of plant downtime would enable plant managers to develop suitable contingency plans to reduce the impact of downtime. This paper presents a stochastic mathematical modelling methodology (more specifically, probability density function of random numbers) which predicts the probable magnitude of ‘the next’ breakdown, in terms of duration for tracked hydraulic excavators. A random sample of 33 machines was obtained from opencast mining contractors, containing 1070 observations of machine breakdown duration. Utilization of the random numbers technique will engender improved maintenance practice by providing a practical methodology for planning, scheduling and controlling future plant resource requirements. The paper concludes with direction for future research which aims to: extend the model's application to cover other industrial settings and plant items; to predict the time at which breakdown will occur (vis‐à‐vis the duration of breakdown); and apply the random numbers modelling to individual machine compartments.

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.

Excavator productivity calculations embrace myriad variables, which in turn, can be modelled in several ways. A key productivity variable is operator competence (O _c ) because this can impact on so many of the other variables. Earlier research has studied excavator productivity, but little has attempted to simultaneously model productivity variables in relation to O _c . The purpose of this paper is to address the void in extant literature.

A numeric, theoretical analysis is undertaken using the Caterpillar® hydraulic excavator productivity model to estimate excavator production, given: first, variance in modifying factors based on derived maximum and minimum values; and second, variance resulting from linear calculations based on excavator operator competence.

Excavator productivity resulting from incremental variance of modifying factors in isolation is shown to be linear except, in the case of bucket payload. Simultaneous application of modifying variables results in a greater, curvilinear productivity trend; while it is demonstrated that quantification of key modifying factors can to a significant extent be related to operator competence.

Findings add to productivity literature generally and to that of plant and equipment more specifically. Results will help productivity estimation of excavation in a practical sense while informing subsequent design of an empirical academic research of this problem.

Originality relates principally to determining modifying factor ranges and their analysis of simultaneous effect on each other, especially, as influenced on assumptions of operator competence.

This study aims to assess the usability of augmented reality (AR) based mobile app for excavation and earthmoving processes using a novel tool entitled Excavator Augmented Reality (EAR).

A mixed-methods research approach was used through conducting experimentation to collect qualitative and quantitative data collected from the Sri Lankan construction sector. EAR app was used for experimentation in outdoor areas examining how a 360° tracked hydraulic excavator can be navigated in different physical environments similar to the real prospected job.

The findings reveal that EAR could make a considerable impact on enhancing productivity, safety and training processes. However, the developed EAR App subjected to assessment demonstrated the highest satisfaction gap for the auditory aspects. Among the remaining criterion, the satisfaction met user expectations for comfortability and no-risk practice. An analysis of strengths, weaknesses, opportunities and threats (SWOT analysis) conducted revealed that visualising the excavator activities and the requirements of improved features were the highest agreed strengths and weaknesses of the EAR. Among the opportunities for improvement, the necessity of improving emergency and safety reached the highest agreement. Moreover, the study presented the challenges in introducing mobile augmented reality (MAR) to the construction sector under the political, economic, sociocultural, technological, environmental and legal (PESTEL) model along with solutions to be taken.

This study provides a novel approach to addressing the safety, productivity and training concerns in heavy mobile plants and machinery on construction sites which remains to be unexplored to this end.

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.

Hydraulic excavator cycle time and associated unit costs of excavation for given input estimating data, for machines operating in the UK construction industry, are predicted. Using multiple regression analysis, three variables are identified as accurate predictors of cycle time: machine weight, digging depth and machine swing angle. With a coefficient of determination (R2) of 0.88, a mean percentage error (MPE) of −5.49, and a mean absolute error (MAPE) of 3.67, the cycle time model is robust; this is further validated using chi‐square analysis and Pearson's correlation coefficient (on predicted and actual values of machine cycle time). An illustrative example of the model's application to determine machine productivity is given. The paper concludes with a spreadsheet model for calculating excavation costs (m3 and cost per h) which is able to deal with any combination of the three independent cycle time predictor variables and other estimator's input data.

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.

Using statistics obtained from the Health and Safety Executive, compares accident rates occurring within the UK construction industry to the accident rates occurring within other industries; then assesses and discusses these. Results reveal that the construction industry is arguably the most hazardous industry and has consistently recorded a poor accident record. Off‐highway plant and equipment is a considerable contributor to the industry’s infamous record. Then assesses accidents relating to individual plant items and discusses the underlying reasons for such accidents. Part of the problem stems from poor mechanical design but in the majority of cases the operator is at fault. Training programmes and initiatives have previously attempted to address this problem but regrettably the Provision and Use of Work Equipment regulations do not enforce mandatory training and certification. Provides a potential solution to this problem through the use of psychometric test development. Hypothesizes such an approach as representing a useful technique for both improving the skills and competence of existing operatives, and aiding the selection process when hiring new operatives.

Construction plant maintenance practice and its plant operators are inextricably linked. This is because, unlike plant operating within the manufacturing sector, construction plant is largely dependent upon operator skill and competence to maintain the item in a safe, fully operational condition. Research has previously successfully modelled machine breakdown, but revealed that the operator’s impact upon machine breakdown rates can be considerable. A conceptual model methodology with which to assess the maintenance proficiency of individual plant operators is presented. Specifically, an artificial intelligent classification model is proposed as a means of classifying plant operator maintenance proficiency into one of three bandings. These are good, average and poor. The results of such work will form the basis of new prescriptive guidelines, for incorporation into the new certificate of training achievement (CTA) scheme, available to inexperienced construction plant operators. The paper concludes with an indication of the palpable benefits of such research, to plant owners and the construction industry at large.