ISLESI 2017 Short Course
1. Introduction of Life-cycle Assessment
2. Life-cycle Performance Assessment for Reinforced Concrete Structures
3. Life-cycle Maintenance Strategy for Reinforced Concrete Structures in Taiwan
1. Introduction of reliability analysis and its application in life cycle analysis
2. Reliability analysis: moment-based approaches
3. Reliability analysis: simulation-based approaches
Approach –All concepts and methods will be illustrated with problems of relevance to engineering
or physical sciences.
Main objectives and Motivation – To model uncertainties and assess their effects on performance
and design of engineering systems. Encourage and appreciate the
role of probabilistic models in engineering.
On uncertainties in engineering – Uncertainties are unavoidable in engineering. These may be divided into two broad types; namely,
Aleatory – data-based; these are inherent randomness orvariability which are part of nature; not
Epistemic – knowledge-based; associated with our inability to accurately predict or estimate reality.
Can be reduced through improved models, better judgments, better experiments.
Outline of Topics
Random variables and probability distributions
Fundamentals of Engineering Reliability and Reliability-Based Design
The recently published ISO2394:2015 contains a new informative Annex D on “Reliability of Geotechnical Structures”. Despite the increasing push for reliability-based design (RBD) in geotechnical engineering, it is accurate to say that the general practitioner is largely unfamiliar with RBD and its potential benefits. There are two main reasons. One, there is lack of statistical guidance on how to characterize geotechnical variability which is a necessary input to RBD. Two, the general practitioner does not know how to calculate the reliability index even if there are sufficient statistical data on hand.
The focus of this lecture is “how to choose realistic statistical inputs” for geotechnical design parameters. However, some background on RBD and how it can be applied to a simple design example will be explained step-by-step using EXCEL. This lecture hopes to explain that there are sufficient statistics and simple calculation tools to apply RBD in geotechnical practice.
This lecture is suitable for students and engineers. Only basic knowledge contained in a standard undergraduate text on statistics and probability is needed. The lecture is based in part on the following book:
Phoon, K. K. & Retief, J. V. (2016). Reliability of Geotechnical Structures in ISO2394, CRC Press/Balkema, 230 p. (Order www.crcpress.com with discount code EQR25 to save 20%)
Participants are strongly encouraged to bring along their laptop with EXCEL installed so that they can actively participate in a hands-on session
2) Life-cycle cost approach for civil engineering problems
3) Examples for structures under wind, earthquake and corrosion hazards
A key consideration in performing lifecycle engineering is to estimate the remaining life of structures in probabilistic terms. The lecture will focus on this aspect of the challenge, and will not cover all other important details of lifecycle engineering due to the limited time. Other speakers would cover these other details. This lecture presents a methodology for the structural reliability analysis of marine vessels based on failure modes of their hull girders, stiffened panels including buckling, fatigue, and fracture and corresponding life predictions at the component and system levels. Factors affecting structural integrity such as operational environment and structural response entail uncertainties requiring the use of probabilistic methods to estimate reliabilities associated with various alternatives being considered for design, maintenance, and repair. Variability of corrosion experienced on marine vessels is a specific example of factors affecting structural integrity requiring probabilistic methods. The Structural Life Assessment of Ship Hulls (SLASH) methodology developed in this paper produces time-dependent reliability functions for hull girders, stiffened panels, fatigue details, and fracture at the component and system levels. The methodology was implemented as a web-enabled, cloud-computing-based tool with a database for managing vessels analyzed with associated stations, components, details, and results, and users. Innovative numerical and simulation methods were developed for reliability predictions with the use of conditional expectation. Examples are provided to illustrate the computations.
Decisions regarding structures should be supported by an integrated life-cycle multi-objective optimization framework by considering, among other factors, the likelihood of successful performance and the total expected cost accrued over the entire life-cycle. The primary objective of this lecture is to highlight an approach for the optimum life-cycle management of aging structures under uncertainty.