Login | Register

Integrated Decision Support Methodology for Bridge Deck Management under Performance-Based Contracting

Title:

Integrated Decision Support Methodology for Bridge Deck Management under Performance-Based Contracting

Alsharqawi, Mohammed ORCID: https://orcid.org/0000-0001-8249-4718 (2018) Integrated Decision Support Methodology for Bridge Deck Management under Performance-Based Contracting. PhD thesis, Concordia University.

[img]
Text (application/pdf)
Alsharqawi_PhD_F2018.pdf - Accepted Version
Restricted to Repository staff only until 11 September 2020.
Available under License Spectrum Terms of Access.
6MB

Abstract

Bridges are vital elements of the civil infrastructure system in terms of mobility, environment, economy, and development of communities. Maintaining bridges at sufficient functional and safety levels is an important mandate to ministries of transportation. The 2016 Canada infrastructure report card alarmed that more than 26% of bridges in Canada have deteriorated and the bridges are mostly rated as fair, poor or very poor (CIRC 2016). In the United States, the report card on America’s infrastructure assigned grade “C+” to bridge infrastructure (ASCE 2017). Hence, developing rational decision support methods that can assist in managing the vast bridge infrastructure is of paramount importance. This research aimed toward developing a decision support methodology for concrete bridges capable for optimizing the Maintenance, Repair and Replacement (MRR) actions under Performance-Based Contracting (PBC) arrangement through implementing the following steps: i) develop an integrated condition assessment and rating model, ii) develop a forecasting model to assess bridge condition reliability and predict future deteriorations/improvements, iii) develop short- and long-term optimized rehabilitation plans, and iv) design a PBC-based framework for rehabilitation decisions. Upon studying bridge inspection standards and current practices, the research introduces the Quality Function Deployment (QFD) theory and Weibull Distribution Function (WDF) to produce novel methods to rate the current bridge conditions and forecast future performance. These methods integrate data extracted from visual inspection and Ground Penetrating Radar (GPR) surveys. The k-means clustering technique is utilized to develop a rating index that recommends suitable MRR actions based on an integrated condition rating. The Genetic Algorithm (GA) optimization technique is applied to select the best combinations of rehabilitation strategies under the PBC scheme. The integrated rating along with the GA optimization ultimately develop a recommended work program that considers the identified performance triggers and budget constraints. The research contributes a novel PBC-based decision support framework to the area of bridge management that enhances efficiency in implementing MRR strategies while maintaining the delicate balance between the different stakeholders’ requirements and goals. The developed methodology is implemented and tested on data extracted from bridge inspection reports and GPR scans, mainly on bridges in Quebec, Canada. Ministries of transportation can benefit from the condition rating and deterioration modeling to assess their bridges’ condition and to interfere and do a rehabilitation action before reaching the end of useful service life. The GA-based model provides the maintenance contractors with optimized interventions plans that specify what type of MRR actions to do and when. Further, it assists the ministries to set the budget for such projects. The PBC framework is expected to assist both the transportation agencies and maintenance contractors in arriving at a fair contract value while maintaining the desired bridge performance.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Alsharqawi, Mohammed
Institution:Concordia University
Degree Name:Ph. D.
Program:Building Engineering
Date:April 2018
Thesis Supervisor(s):Haghighat, Fariborz
ID Code:984398
Deposited By: Mohammed N. S. Alsharqawi
Deposited On:31 Oct 2018 17:46
Last Modified:31 Oct 2018 17:46
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top