Mohammed S. El-Abbasy

Contractor Selection Model for Highway Projects Using Integrated Simulation and Analytic Network Process

AbstractSelecting the appropriate contractor is a significant step to highway project success. The process of selection is subjected to uncertainty and influence of criteria other than bid price. This paper presents an approach to prioritize competitive contractors at the prebidding stage for highway projects by utilizing the analytic network process (ANP) and Monte Carlo simulation. Both techniques are integrated on a single platform to build the proposed model. Initially, the main quantitative and qualitative criteria affecting the contractors’ selection process are identified and studied. The effective criteria in the selection process were obtained from experts and literature. A structured questionnaire was designed and sent to experts in highway projects. The ANP was used to prioritize these criteria subsequently, and Monte Carlo simulation was utilized to develop the selection model. The applicability of the proposed model was tested using four real cases of highway projects. It was concluded that u...

Condition Prediction Models for Oil and Gas Pipelines Using Regression Analysis

Although they are the safest means of transporting oil and gas products, pipelines can sometimes fail with hazardous consequences and large business losses. The decision to replace, repair, or rehabilitate depends mainly on the condition of the pipeline. Assessing and predicting its condition is therefore a key step in the maintenance plan of a pipeline. Several models have recently been developed to predict pipeline failures and conditions. However, most of these models were limited to the use of corrosion as the sole factor to assess the condition of pipelines. The objective of this paper is to develop models that assess and predict the condition of oil and gas pipelines based on several factors including corrosion. The regression analysis technique was used to develop the condition prediction models based on historical inspection data of three existing pipelines in Qatar. In addition, a condition assessment scale for pipelines was built based on expert opinion. The models were able to satisfactorily predict pipeline condition with an average percent validity above 96% when applied to the validation data set. The models are expected to help decision makers assess and predict the condition of existing oil and gas pipelines and hence prioritize their inspection and rehabilitation planning.

A condition assessment model for oil and gas pipelines using integrated simulation and analytic network process

Even though they are safe and economical transportation means of gas and oil products around the world, pipelines can be subject to failure and degradation generating hazardous consequences and irreparable environmental damages. Therefore, gas and oil pipelines need to be effectively monitored and assessed for optimal and safe operation. Many models have been developed in the last decade to predict pipeline failures and conditions. However, most of these models used corrosion features as the sole factor to assess the condition of pipelines. Therefore, the objective of this paper was to develop a condition assessment model of oil and gas pipelines that considers several factors besides corrosion. The proposed model, which uses both analytic network process and Monte Carlo simulation, considers the uncertainty of the factors affecting pipeline condition and the interdependency relationships between them. The performance of the model was tested on an existing offshore gas pipeline in Qatar and was found to be satisfactory. The model will help pipeline operators to assess the condition of oil and gas pipelines and hence prioritise their inspections and rehabilitation requirements.

Condition-based maintenance decision support system for oil and gas pipelines

The 2013 report card of Americas infrastructure has scored the condition of oil and gas pipelines as D+ which means that such pipelines are in a relatively poor condition. More than 10,000 failures have been recorded in the US. These failures have resulted in environmental, health and property damages. Therefore, there is a definite need to give more attention to the maintenance of oil and gas pipelines. This paper develops a comprehensive model for the maintenance planning of oil and gas pipelines. The model selects rehabilitation/repair alternatives for oil and gas pipelines based on their condition during their service life. These alternatives are then used to calculate the cash flow throughout the service life of these infrastructures. The model, which uses Monte Carlo simulation and fuzzy approach to address the uncertainties in the estimation of the maintenance operation costs and the economic parameters, calculates the Equivalent Uniform Annual Worth of the identified alternatives. The optimum maintenance programmes consist of the alternatives that have the lowest life cycle cost of oil and gas pipelines. The model is expected to support pipeline operators in the maintenance decision-making process of oil and gas pipelines.

FINANCE-BASED SCHEDULING FOR MULTIPLE PROJECTS WITH MULTIMODE ACTIVITIES

Lack of financing and cash deficit is considered as a primary threat to contractor’s financial management. Therefore, in case of insufficient cash, many contractors find it difficult to stick with the project schedule leading to extra overhead costs and liquidated damages. Mainly contractors deal with the project scheduling and financing as two independent functions of construction project management. Thus, the main objective of this research is to develop a multi-objective elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for solving finance-based scheduling problem of multi-projects with multi-mode activities. A CPM scheduling model is constructed with its associated cash flow to calculate the values of the multiple objectives. The problem involves the minimization of conflicting objectives: duration of multiple projects, financing costs, and maximum negative cumulative balance. The designed optimization model performs operations of NSGA-II in three main phases: (1) Population initialization; (2) Fitness evaluation; and (3) Generation evolution. An application example is analyzed to illustrate the use of the model and to demonstrate its capabilities in generating optimum solutions. The model can be considered relevant for practitioners to use in large construction projects to make decisions regarding financing.

Integrated performance assessment model for water distribution networks

Abstract The American infrastructure report card in 2013 rated the US water system infrastructure with grade of ‘D’. The Canadian infrastructure report card in 2012 stated that around 15.4% of Canada’s water infrastructure has a condition of fair to very poor. Thus, there is a critical need to develop efficient inspection, maintenance and rehabilitation plans for water distribution networks. However, such plans require an assessment tool to evaluate the performance and condition of water distribution networks. Therefore, the main purpose of this paper is to develop an integrated performance assessment model for water distribution networks. Two modules were developed to assess the performance of water pipelines and accessories, respectively. A third module was developed to assess the performance of water segments that includes pipelines and accessories. Moreover, a fourth module was built to assess the performance of each sub-network and the entire network based on the segments’ connection type using a reliability-based approach. To assess the performance of the water distribution network, the critical factors affecting its pipelines and accessories were identified and studied. The fuzzy analytic network process technique was used to obtain the importance weights of the identified factors.

Evidential Reasoning–Based Condition Assessment Model for Offshore Gas Pipelines

Condition assessment of oil and gas pipelines is a significant component in pipeline operations and maintenance. Such assessments are used to ensure better decisions for repair and/or replacement to reduce pipelines’ failure possibilities. Therefore, it is essential to have an effective condition assessment model for pipelines as their failure incidents may lead to catastrophic, economical, and environmental consequences. Current practices of assessing gas pipelines condition can be considered simplified for the intended purpose. They mainly depend on experts’ opinions in interpreting inspection data, where the process is influenced by human subjectivity and reasoning uncertainty. In other words, they need detailed knowledge on the translation of raw inspection data into valuable information. This will surely lead to decisions lacking thorough and extensive review of the most influential aspects on pipelines’ conditions. To address the weaknesses of current practices, this research proposes a new fuzzy-based methodology that utilizes an integrated analytic network process (ANP) and hierarchical evidential reasoning (HER) to develop a meticulous condition assessment model for offshore gas pipelines. The proposed model is validated using historical inspection reports that are obtained from a local pipeline operator in Qatar. The model delivers satisfactory outcomes in assessing offshore gas pipelines’ conditions based on real field data.

Unpiggable Oil and Gas Pipeline Condition Forecasting Models

AbstractAlthough they are the safest method of transporting oil and gas, pipelines are still subject to different degrees of failure and degradation. It is therefore important to efficiently monitor oil and gas pipelines to optimize their operations and to reduce their failures to an acceptable safety limit. Several models have recently been developed to predict oil and gas pipeline failures and conditions. However, most of these models were limited to the use of corrosion features as the sole factor in assessing pipeline condition. In addition, the use of internal corrosion features in the condition assessment requires the pipe to be piggable, which is not always the case. Modifying pipelines with pigging facilities is not always an easy option and can be very costly and time consuming. This paper presents the development of condition forecasting models for unpiggable oil and gas pipelines based on factors other than those related to internal corrosion. In addition, the paper examines the degree of confi...

Locating Leaks in Water Mains Using Noise Loggers

Qatar National Research Fund (QNRF) for this research project under Award No. QNRF-NPRP 4-529-2-193.

Generic Scheduling Optimization Model for Multiple Construction Projects

AbstractIn the construction industry, contractors usually manage and execute multiple projects simultaneously. Typically, this situation involves sharing different types of resources, including cas...