Estimating project and activity duration: a risk management approach using network analysis

Abstract

Variations in the durations of activities are commonplace in the construction industry. This is due to the fact that the construction industry is influenced greatly by variations in weather, productivity of labour and plant, and quality of materials. Stochastic network analysis has been used by previous researchers to model variations in activities and produce more effective and reliable project duration estimates. A number of techniques have been developed in previous literature to solve the uncertain nature of networks, these are: PERT (program evaluation and review techniques), PNET (probabilistic network evaluation technique), NRB, (narrow reliability bounds methods) and MCS (Monte Carlo simulation). Although these techniques have proved to be useful in modelling variations in activities, dependence of activity duration is not considered. This can have a severe impact on realistically modelling projects. In this context, the objective of the present research is to develop a methodology that can accurately model activity dependence and realistically predict project duration using a risk management approach. A simulation model has been developed to encapsulate the methodology and run experimental work. In order to achieve this, the following tasks are tackled: identify risk factors that cause activity variations using literature reviews and conducting interviews with contractors; model risk factors and their influence on activity variations through conducting case studies and identifying any dependence between them; develop a computer based simulation model that uses a modified Monte Carlo technique to model activity duration and dependence of risk factors; and run experimental work to validate and verify the model.Network Analysis, Monte Carlo Simulation, Pert, Stochastic Analysis,

Development of a methodology for analysing and quantifying the impact of delay factors affecting construction projects

“…Existing risk analysis models, for example, simple analytical and neural networks developed by Kog et al 1999 Jaskowski and Biruk (2011) agreed that the simulation model developed by Dawood (1998) is a quantitative delay analysis model which considered the impact of each delay factor independently for predicting durations of activities and whole project. Therefore, the research presented in this paper seeks to integrate the delay factors within the simulation model of delay analysis system to predict the activity or project durations, considering the influence of each risk factor independently.…”

“…There are different types of random distribution values such as uniform, triangular, normal and beta. The type of distribution can vary from one activity to another activity (Dawood, 1998). However, this depends on the types of (risk) delay factors and their nature of impact on each critical activity in a project and these aspects were considered for assuming the risk distribution for delay analysis in this system.…”

Methodology for enhancing reliability of predictive project schedules in construction

“…The impact of particular risk factors is the object of analysis of many researchers [6,25,26]. Certainly, it is project-specific.…”

“…To determine actual distribution type and parameters of a construction process duration, one could conduct series of time-consuming and costly measurements on site. With these being usually unavailable, the researchers base on simplifying assumptions [6,19,25,26].…”

“…Dawood [6] proposed a simulation-based method to model correlation of process durations as random variables and to improve precision of project duration estimates. Defining duration risk factors as well as assessing their impact on process times are experience based, which requires access to expert knowledge and historical records.…”