Productivity is described as the quantitative measure between the number of resources used and the output produced, generally referred to man-hours required to produce the final product in comparison to planned man-hours. Productivity is a key element in determining the success and failure of any construction project. Construction as a labour-driven industry is a major contributor to the gross domestic product of an economy and variations in labour productivity have a significant impact on the economy. Attaining a holistic view of labour productivity is not an easy task because productivity is a function of manageable and unmanageable factors. Compound irregularity is a significant issue in modeling construction labour productivity. Artificial Neural Network (ANN) techniques that use supervised learning algorithms have proved to be more useful than statistical regression techniques considering factors like modeling ease and prediction accuracy. In this study, the expected productivity considering environmental and operational variables was modeled. Various ANN techniques were used including General Regression Neural Network (GRNN), Backpropagation Neural Network (BNN), Radial Base Function Neural Network (RBFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) to compare their respective results in order to choose the best method for estimating expected productivity. Results show that BNN outperforms other techniques for modeling construction labour productivity.

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One of the main reasons for pipeline breakage and leakage is aging. This results in a decline in system efficiency, and associated levels of service, which in turn may endanger public health and safety. Since the most expensive and significant part of a water supply system is the distribution network, repair strategies are necessary to protect the value of the assets when a pipeline reaches the end of its useful life. Usually, repair works are taken as a reaction to the detection of a leak, pressure improvement and other factors that eventually results in an inefficient management of allocated funds. Therefore, a careful computational analysis should be performed to efficiently utilize the allocated budge. This paper presents a near‐optimized budget allocation model that is able to find the near‐optimal scheduling plan for renewal and/or replacement. The aim of this paper is to suggest a new model in order to minimize the number of breaks over a given planning horizon and the overall cost of repair of water distribution networks (WDNs), including indirect damage cost, direct damage cost, and failure repair cost while fulfilling functional requirements. The model will identify the pipe segment that needs replacement, time of replacement, and the necessary interventions that should be carried out for the network. It also identifies those pipelines that need repair. The solution is usually constrained by the yearly limited budget. The optimization procedure results in a solution alternative that decision‐makers could use for their operational requirements. The outcome of the developed model is validated based on the available leakage and breakage data from the city of Montreal database and an existing validated model. The model forecast that, in the next 20 years, 19.7% of the network will need open trench repair, 25.3% could be repaired using trenchless techniques, while 50.9% of the pipelines will remain intact.