Ibrahim Bakry

Decision Support Model for Integrated Risk Assessment and Prioritization of Intervention Plans of Municipal Infrastructure

AbstractThis paper presents a model for the integrated risk-based prioritization of municipal infrastructure assets. The model is a three-module decision-making tool for planning risk-based rehabilitation of water and sewer networks sharing the same corridor. The model is developed to identify corridor segments, assess risk of individual and integrated asset networks, and to set priorities for intervention plans of related critical corridor segments. The probability of failure of water pipe segments is calculated utilizing data from municipal inspection reports, while the probability of failure of sewer pipe segments is determined by soliciting experts’ opinions. The consequences of failure for individual water and sewer networks account for 13 economic, social, and environmental factors. Risk matrices are used to determine the criticality index of water and sewer segments depending on the combinations of probability and consequences of failure for each network measured on an ordinal scale. To integrate w...

Fuzzy dynamic programming for optimized scheduling of repetitive construction projects

Uncertainty is an inherent characteristic of construction projects. Neglecting uncertainties associated with different input parameters in the planning stage could well lead to misleading and unrealistic project schedules. This research presents an algorithm for optimized scheduling of repetitive construction projects under uncertainty. The research utilizes fuzzy set theory to model uncertainties associated with different input parameters. It employs a dynamic programming algorithm that is especially tailored to accept input for different variables, perform all necessary computations and successfully deliver output, all in terms of fuzzy numbers. The algorithm has the ability to identify the optimum crew formation that would yield project least cost or project shortest duration according to the user preferences. A case study is drawn from literature and analyzed to demonstrate the algorithms capabilities and to allow comparison of results to those generated using previous techniques.

Condition Prediction for Chemical Grouting Rehabilitation of Sewer Networks

AbstractDifferent techniques have been used to maintain and rehabilitate pipes and manholes of the sewer networks in the province of Quebec, including several trenchless rehabilitation techniques in the past two decades. In an effort to predict the future performance of trenchless rehabilitations, this paper presents condition prediction models for chemical grouting rehabilitation of both pipelines and manholes in the city of Laval, Quebec, Canada. The models were developed using regression analysis, based on gathered and analyzed closed circuit television (CCTV) inspection reports for the Laval city sewer network. Different defects in the chemical grouting rehabilitated sewer mains and manholes in this city are presented. The developed regression models are capable of predicting the structural and operational conditions; they are also utilized to generate deterioration curves over time for chemical grouting rehabilitation of sewer pipes and manholes based on basic governing factors such as pipe material ...

Condition Prediction for Cured-in-Place Pipe Rehabilitation of Sewer Mains

AbstractAuthorities in Quebec, Canada, have been making considerable efforts to improve sewer networks across the province by using up-to-date technologies. To rehabilitate its sewer mains, Quebec has used several rehabilitation methods including slip lining, cement and epoxy lining, and cured-in-place pipe (CIPP).These replacement and rehabilitation techniques have been developed over the last four decades, with many arbitrary declarations made about the efficiency and performance of different rehabilitation techniques. This paper presents condition prediction models for CIPP rehabilitation of sewer mains. Regression analysis technique is used to develop these models, based on gathered and analyzed closed-circuit television (CCTV) inspection reports for Quebec CIPP rehabilitations. The models can predict the structural and operational conditions of CIPP rehabilitation on the basis of basic input such as pipe material, and rehabilitation type and date. They can also generate curves illustrating condition ...

Optimized scheduling and buffering of repetitive construction projects under uncertainty

Purpose Construction projects are complex projects taking place in dynamic environments, which necessitates accounting for different uncertainties during the planning stage. There is a significant lack of management tools for repetitive projects accounting for uncertainties in the construction environment. The purpose of this paper is to present an algorithm for the optimized scheduling of repetitive construction projects under uncertainty. Design/methodology/approach Fuzzy set theory is utilized to model uncertainties associated with various input parameters. The developed algorithm has two main components: optimization component and buffering component. The optimization component presents a dynamic programming approach that processes fuzzy numbers. The buffering component converts the optimized fuzzy schedule into a deterministic schedule and inserts time buffers to protect the schedule against anticipated delays. Agreement Index (AI) is used to capture the user’s desired level of confidence in the produced schedule while sizing buffers. The algorithm is capable of optimizing for cost or time objectives. An example project drawn from literature is analysed to demonstrate the capabilities of the developed algorithm and to allow comparison of results to those previously generated. Findings Testing the algorithm revealed several findings. Fuzzy numbers can be utilized to capture uncertainty in various inputs without the need for historical data. The modified algorithm is capable of optimizing schedules, for different objectives, under uncertainty. Finally AI can be used to capture users’ desired confidence in the final schedule. Originality/value Project planners can utilize this algorithm to optimize repetitive projects schedules, while modelling uncertainty in different input parameters, without the need for relevant historical data.