Solomon Tesfamariam

A review of multi-criteria decision-making methods for infrastructure management

In infrastructure management, multi-criteria decision-making (MCDM) has emerged as a decision support tool to integrate various technical information and stakeholder values. Different MCDM techniques and tools have been developed. This paper presents a comprehensive review on the application of MCDM literature in the field of infrastructure management. Approximately 300 published papers were identified that report MCDM applications in the field of infrastructure management during 1980–2012. The reviewed papers are classified into application to the type of infrastructure (e.g. bridges and pipes), and prevalent decision or intervention (e.g. repair and rehabilitate). In addition, the papers were also classified according to MCDM methods used in the analysis. The paper provides taxonomy of those articles and identifies trends and new developments in MCDM methods. The results suggest that there is a significant growth in MCDM applications in infrastructure management applications of MCDM over the last decade. It has also been noted that many decision support tools based on multiple MCDM methods have been successfully used for infrastructure management.

Probability density functions based weights for ordered weighted averaging (OWA) operators: An example of water quality indices

Abstract This paper explores the application of ordered weighted averaging (OWA) operators to develop water quality index, which incorporates an attitudinal dimension in the aggregation process. The major thrust behind selecting the OWA operator for aggregation of multi-criteria is its capability to encompass a range of operators bounded between minimum and maximum . A new approach for generating OWA weight distributions using probability density functions (PDFs) is proposed in this paper. The basic parameters (mean and standard deviation) of the probability density functions can be determined using the number of criteria (e.g., water quality indicators) in the aggregation process. The proposed approach is demonstrated using data provided in a study by Swamee and Tyagi [Swamee, P.K., Tyagi, A., 2000. Describing water quality with aggregate index. ASCE Journal of Environmental Engineering 126 (5), 451–455] for establishing water quality indices. The Normal distribution and its inverse form were found suitable for compromising or normative decisions, whereas the Exponential and its inverse form were found suitable for pro-risk and risk-averse decisions, respectively. The proposed OWA weight distributions are also compared with the commonly used regular increasing monotone (RIM) functions for generating OWA weights. Sensitivity analyses are carried out to highlight the utility of the proposed approach for multi-criteria decision-making and establishing water quality indices.

A survey of non-gradient optimization methods in structural engineering

In this paper, we present a review on non-gradient optimization methods with applications to structural engineering. Due to their versatility, there is a large use of heuristic methods of optimization in structural engineering. However, heuristic methods do not guarantee convergence to (locally) optimal solutions. As such, recently, there has been an increasing use of derivative-free optimization techniques that guarantee optimality. For each method, we provide a pseudo code and list of references with structural engineering applications. Strengths and limitations of each technique are discussed. We conclude with some remarks on the value of using methods customized for a desired application.

Evaluating risk of water mains failure using a Bayesian belief network model

It has been reported that since year 2000, there have been an average 700 water main breaks per day only in Canada and the USA costing more than CAD 10 billions/year. Moreover, water main leaks affect other neighboring infrastructure that may lead to catastrophic failures. For this, municipality authorities or stakeholders are more concerned about preventive actions rather reacting to failure events. This paper presents a Bayesian Belief Network (BBN) model to evaluate the risk of failure of metallic water mains using structural integrity, hydraulic capacity, water quality, and consequence factors. BBN is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. The proposed model is capable of ranking water mains within distribution network that can identify vulnerable and sensitive pipes to justify proper decision action for maintenance/rehabilitation/replacement (M/R/R). To demonstrate the application of proposed model, water distribution network of City of Kelowna has been studied. Result indicates that almost 9% of the total 259 metallic pipes are at high risk in both summer and winter.

Risk-Based Seismic Evaluation of Reinforced Concrete Buildings

Seismic resiliency of new buildings has improved over the years due to improved seismic codes and design practices. However, vulnerability of seismically deficient older buildings, designed and built on the basis of older codes of practices, poses a significant threat to life safety and survivability of buildings. It is economically not feasible to retrofit the entire inventory of seismically deficient buildings. Therefore, there is need for a comprehensive plan to identify critical buildings and prioritize their retrofit and upgrading requirements. A risk-based evaluation technique is proposed in this paper to quantify seismic assessment and develop a ranking scheme for reinforced concrete buildings. The seismic hazard, building vulnerability and consequence of failure are handled in hierarchical structures. Some of the input risk parameters, expressed as qualitative and quantitative quantifiers, are transformed into commensurable values. A knowledge-based fuzzy rule base modelling is developed and verified through the use of 1994 Northridge Earthquake data on seismic damage of reinforced concrete buildings.

Seismic Vulnerability Assessment of Reinforced Concrete Buildings Using Hierarchical Fuzzy Rule Base Modeling

A reliable building vulnerability assessment is required for developing a risk-based assessment and retrofit prioritization. Tesfamariam and Saatcioglu (2008) proposed a simple building vulnerability module where the building performance modifiers are in congruence with FEMA 154. This paper is an extension of the building vulnerability assessment that include detailed performance modifier in congruence with FEMA 310 that is represented in a heuristic based hierarchical structure. Some of the input parameters are obtained through a walk down survey and are subject to vagueness uncertainty that is modelled through fuzzy set theory. A knowledge base fuzzy rule base modeling is developed and illustrated for reinforced concrete buildings damaged in the 1 May 2003 Bingöl, Turkey earthquake.

Models for Seismic Vulnerability Analysis of Power Networks: Comparative Assessment

Electric power networks are spatially distributed systems, subject to different magnitude and recurrence of earthquakes, that play a fundamental role in the well-being and safety of communities. Therefore, identification of critical components is of paramount importance in retrofit prioritization. This article presents a comparison of five seismic performance assessment models (M1 to M5) of increasing complexity. The first two models (M1 and M2) approach the problem from a connectivity perspective, whereas the last three (M3 to M5) consider also power flow analysis. To illustrate the utility of the five models, the well-known IEEE-118 test case, assumed to be located in the central United States, is considered. Performances of the five models are compared using both system-level and component-level measures. Spearman rank correlation ρ is computed between results of each model. Highest ρ values, at both system- and component-level, are obtained, as expected, between M1 and M2, and within models M3 to M5. The ρ values between component-level measures are relatively high across all models, indicating that simpler ones (M1 and M2) are appropriate for vulnerability assessment and retrofit prioritization. The complex flow-based models (M3 to M5) are suitable if actual performance of the systems is desired, as it is the case when the power network is considered within a larger set of interconnected infrastructural systems.

Selection of remedial alternatives for mine sites: a multicriteria decision analysis approach.

The selection of remedial alternatives for mine sites is a complex task because it involves multiple criteria and often with conflicting objectives. However, an existing framework used to select remedial alternatives lacks multicriteria decision analysis (MCDA) aids and does not consider uncertainty in the selection of alternatives. The objective of this paper is to improve the existing framework by introducing deterministic and probabilistic MCDA methods. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods have been implemented in this study. The MCDA analysis involves processing inputs to the PROMETHEE methods that are identifying the alternatives, defining the criteria, defining the criteria weights using analytical hierarchical process (AHP), defining the probability distribution of criteria weights, and conducting Monte Carlo Simulation (MCS); running the PROMETHEE methods using these inputs; and conducting a sensitivity analysis. A case study was presented to demonstrate the improved framework at a mine site. The results showed that the improved framework provides a reliable way of selecting remedial alternatives as well as quantifying the impact of different criteria on selecting alternatives.

A fuzzy Bayesian belief network for safety assessment of oil and gas pipelines

Abstract Safety assessment of oil and gas (O&G) pipelines is necessary to prevent unwanted events that may cause catastrophic accidents and heavy financial losses. This study develops a safety assessment model for O&G pipeline failure by incorporating fuzzy logic into Bayesian belief network. Proposed fuzzy Bayesian belief network (FBBN) model explicitly represents dependencies of events, updating probabilities and representation of uncertain knowledge (such as randomness, vagueness and ignorance). The study highlights the utility of FBBN in safety analysis of O&G pipeline because of its flexible structure, allowing it to fit a wide variety of accident scenarios. The sensitivity analysis of the proposed model indicates that construction defect, overload, mechanical damage, bad installation and quality of worker are the most significant causes for the O&G pipeline failures. The research results can help owners of transmission and distribution pipeline companies and professionals to prepare preventive safety measures and allocate proper resources.

Seismic Vulnerability of Reinforced Concrete Frame with Unreinforced Masonry Infill Due to Main Shock–Aftershock Earthquake Sequences

This paper presents a parametric study on the inelastic response of six-story reinforced concrete (RC) frames subjected to main shock–aftershock (MS-AS) ground motions. For this purpose, one bare frame (BF) and four masonry RC frames with two infill thicknesses (75 mm or 125 mm) and two infill patterns (open ground story or fully infilled) are considered. They are situated at site class C in Vancouver, Canada, and are designed for office occupancy according to the 2005 National Building Code of Canada. The five frames are subjected to 100 ensembles of MS-AS ground motions scaled to seismic hazard level corresponding to the return period of 2,500 years. For each sequence of earthquakes, change in the fundamental period (T1) and inter-story drift (ISD) for both MS-AS sequences are quantified. The analysis results show that the period change and ISD were significant for BF, whereas the infilled frames sustained small damage with negligible change in T1.