Part One. Multivariate distributions. Preliminary data analysis. Part Two: Finding new underlying variables. Principal component analysis. Factor analysis. Part Three: Procedures based on the multivariate normal distribution. The multivariate normal distribution. Procedures based on normal distribution theory. The multivariate analysis of variance. The multivariate analysis of covariance and related topics. Part Four: Multi-dimensional scaling and cluster analysis. Multi-dimensional scaling. Cluster analysis.

Introduction to multivariate analysis, by C. Chatfield and A. J. Collins. Pp 246. £13 hardcover, £7·50 paperback. 1980. ISBN 0-412-16030-7/4 (Chapman and Hall)

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.

The state of health and safety in the UK construction industry with a focus on plant operators

Forecasting is an integral part of supply chain management, which improves information coordination and reduces bullwhip effect. In this paper, we develop a two-stage meta-heuristics based ARMA model that is parsimonious, has high forecast accuracy and is generalizable for time series datasets with no assumptions on seasonality, stationary and normal error distributions. We use simulated annealing to determine the best order of autoregression and order of moving averages. Genetic algorithm is used to determine the best coefficient of autoregression and moving average.

Meta-Heuristics in ARMA Forecasting

Construction plant breakdown affects projects by prolonging duration and increasing costs. Therefore, prediction of plant breakdown, as a precursor to conducting timely maintenance works, cannot be underestimated. This paper thus sought to develop a model for predicting plant breakdown time from a sequence of discrete plant breakdown measurements that follow non‐random orders. An ARIMA (1,1,0) model was constructed following experimentation with exponential smoothening. The model utilised breakdown observations obtained from six wheeled loaders that had operated a total of 14,467 hours spread over a 300‐week period. The performance statistics revealed MAD and RMSE of 5.03 and 5.33 percent respectively illustrating that the derived time series model is accurate in modelling the dependent variable. Also, the F‐statistics from the ANOVA showed that the type and frequency of fault occurrence as a predictor variable is significant on the model's performance at the five percent level. Future work seeks to consider a more in depth multivariate time series analyses and compare/contrast the results of such against other deterministic modelling techniques.

Predicting Construction Plant Breakdown Time Using Time Series Modelling

Labour resources are invaluable assets in the construction industry. Nurturing a quality workforce and promoting stable employment for construction personnel have often been advocated as part and parcel of an industrial policy. Yet, the future labour market of the industry is always uncertain, and there is a need for estimating future labour market conditions as an aid to policy formulation and implementation. The Box‐Jenkins approach has been applied to develop Autoregressive Integrated Moving Average (ARIMA) models to analyse and forecast five key indicators in the construction labour market of Hong Kong: employment level, productivity, unemployment rate, underemployment rate and real wage. This approach can be adopted in more complex and diverse labour markets subject to the properties of the utilized data series. Quarterly time‐series statistics over the period 1983–2002 are used in this study. The predictive adequacy of the models derived is evaluated with out‐of‐sample forecasts in comparison with a...

Time series forecasts of the construction labour market in Hong Kong: the Box‐Jenkins approach

To sustain competitiveness, construction plant manufacturers must be able to accurately forecast market sales and moreover, their anticipated percentage of those sales. This paper addresses the former of these forecasts through the development of a multivariate time series model. Specifically, an autoregressive moving average (ARMA) time series model (otherwise known as the Box‐Jenkins approach) is constructed using economic data relating to a 15‐year period (1985–99). It is identified that population (millions); housing completions total (millions), total building repair and maintenance (billions Euro at 1998 prices, where 1 Euro = £0.6776) and gross domestic product (millions at market prices) are able to accurately predict plant sales. The performance statistics show the derived time series model to be very accurate in modelling the dependent variable [mean absolute deviation (MAD) = −87.04 and root mean square error (RMSE) = 1404.05]. By observing economic interactions and influences on sales, it is envisaged that the manufacturer can adjust their production output to match demand. Based upon the model produced, a forecast of machine sales for year 2000 is made and indicates that sales numbers look set to reduce to 15 482 units. The paper concludes with direction for future research work that will aim to model machine park, the influence of grey machines and individual plant items.

Forecasting UK construction plant sales

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Forecasting UK construction plant sales