Maintenance of the construction equipment fleet being an indispensably important concern in megaprojects such as construction of bridges and dams, equipment reliability metrics such as failure-rates, availability of equipment, time between failures and time required for repair are of paramount interest for contractors and project managers. The availability of data on variables such as time to failure, repair-time and the like motivates the determination of appropriate probability models that

more »

... the data with a high degree of accuracy and facilitate estimation of probabilities that may be valuable in project-planning. Estimation of parameters of the proposed model by efficient estimation procedures is one of the first steps in achieving a model that 'best' fits the data. Only very recently, a property of a particular class of continuous probability distributions that has been named 'self-inversion at unity' has begun to be utilized for obtaining modifications to well-known estimators so that the modified estimators are more efficient than their well-known counterparts. In this paper, we focus on the more general case that we call 'self-inversion at A', where A can be any arbitrary real number, and propose a modification to the formula of the sample mean on the basis of this property. By applying the newly proposed modified mean to a data-set pertaining to repair-times of construction equipment, we demonstrate the usefulness of this approach in achieving probability models that are likely to fit, with a higher degree of accuracy than that which is achievable through the utilization of the well-known estimators, reliability and maintenance-related data encountered in megaprojects undertaken by civil engineers as well as in a variety of other engineering endeavors.