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A scheduling method for determining the critical path in linear projects is presented, that takes into account maximum time and distance constraints in addition to the commonly used minimum time and distance constraints. The maximum constraints, though often present in the specifications of a project, are not considered during the planning procedure, since no method existed to enable scheduling with them. The proposed method builds on the concept of the maximum constraints and expands on the necessary background for their implementation into the schedule. The introduced critical path algorithm allows for grouping linear activities into four categories regarding their critical status and their ability to influence project duration. The method is applied to a low‐pressure pipeline construction project and the results are presented.

The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model's performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA).

An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks.

Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors.

Earthmoving trucks’ sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.