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Explore the importance of system reliability and redundancy in structural engineering, focusing on highway bridges to prevent catastrophic collapses. Learn about progressive collapse, safety criteria, and strategies to minimize risks.
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Reliability and Redundancy Analysis of Structural Systemswith Application to Highway Bridges Michel Ghosn The City College of New York / CUNY
Contributors • Prof. Joan Ramon Casas UPC Construction Engineering • Ms. Feng Miao • Mr. Giorgio Anitori
Introduction • Structural systems are designed on a member by member basis. • Little consideration is provided to the effects of a local failure on system safety. • Local failures may be due to overloading or loss of member capacity from fatigue fracture, deterioration, or accidents such as an impact or a blast. • Local failure of one element may result in the failure of another creating a chain reaction that progresses throughout the system leading to a catastrophic progressive collapse.
I-35W over Mississippi River (2007) Truss bridge Collapse due to initial failure of gusset plate
I-40 Bridge in Oklahoma (2002) Bridge collapse due to barge impact
Route 19 Overpass, Quebec (2006) Box-Girder bridge collapse due to corrosion
Structural Redundancy Bridges survive initial damage due to system redundancy and reserve safety Collisions Fatigue Fracture Seismic Damage
Definitions • Redundancy is the ability of a system to continue to carry loads after the overloading of members. • Robustness is the ability of a structural system to survive the loss of a member and continue to carry some load. • Progressive Collapse is the spread of an initial local failure from element to element resulting, eventually, in the collapse of an entire structure or a disproportionately large part of it.
Deterministic Criteria • Ultimate Limit State • Functionality Limit State • Damaged Limit State
State of the Art • New guidelines to have high levels of redundancy in buildings. • Criteria are based on deterministic analyses. • Uncertainties in estimating member strengths and system capacity as well as applied load intensity and distribution justify the use of probabilistic methods.
Reliability Index, b • Reliability index, b, is defined in terms of the Gaussian Prob. function: • If R and S follow Gaussian distributions: b function of means and standard deviations
Lognormal Probability Model • If the load and resistance follow Lognormal distributions then the reliability index is approximately b function of coefficients of variation: V =stand. Dev./ mean
System Reliability • Probability of structural collapse, P(C), due to different damage scenarios, L, caused by multiple hazards, E: • P(E) =probability of occurrence of hazard E • P(L|E) = probability of local failure, L, given E • P(C|LE) is probability of collapse given L due to E
Safety Criteria • The probability of bridge collapse must be limited to an acceptable level: • Alternatively, the criteria can be set in terms of the reliability index, β, defined as:
Option 1 to Reduce Risk • Reduce exposure to hazards: lower P(E) • Protect columns from collisions through barriers • Set columns at large distances from roadway to avoid crashes • Increase bridge height to avoid collisions with deck • Build away from earthquake faults • Use steel connection details that are not prone to fatigue and fracture failures • Increase security surveillance to avoid intentional sabotage
Option 2 to Reduce Risk • Reduce member failure given a hazard: P(L|E) • Increase reliability of connection details by using different connection types, advanced materials, or improved welding, splicing and anchoring techniques • Strengthen columns that may be subject to collisions or sabotage using steel jacketing or FRP wrapping • Increase capacity of columns and critical members to improve their ability to resist unusual loads
Option 3 to Reduce Risk • Avoid collapse if one member fails: P(C|LE) • Use structural configurations that have high levels of redundancy. • Appropriately spaced large number of columns • Trusses that are not statically determinate • Ensure that all the members contributing to a mode of failure are conservatively designed • to pick up the load shed by member that fails in brittle mode • to pick up additional load applied if member that initiates sequence fails in a ductile mode.
Issues with Reliability Analysis • Realistic structural models involve: • Large numbers of random variables • Multiple failure modes • Low probability of failure for members, 10-4 • Probability of failure for systems, 10-6 • Computational effort
Reliability Analysis Methods • Monte Carlo Simulation (MCS) • First Order Reliability Method (FORM) • Response Surface Method (RSM) • Latin Hypercube Simulation (LHS) • Genetic Search Algorithms (GA) • Subset Simulation (SS)
Monte Carlo Simulation (MCS) • Random sampling to artificially simulate a large number of experiments and observe the results. • Can solve problems with complex failure regions. • Needs large numbers of simulations for accurate results.
Probab. of failure = Number of cases in failure domain/ total number of cases Monte Carlo Simulation (MCS)
First Order Reliability Method • First Order Reliability Method (FORM) approximates limit-state function with a first-order function. • Reliability index is the minimum distance between the mean value to the failure function. • If limit state function is linear
First Order Reliability Method Use optimization techniques to find design point = shortest distance between Z=0 to origin of normalized space
Response Surface Method (RSM) • RSM approximates the unknown explicit limit state function by a polynomial function. • A second order polynomial is most often used for the response surface. • The function is obtained by perturbation of variables near design point.
Subset Simulation (SS) • If F denote the failure domain. Subset failure regions Fi are arranged to form a decreasing sequence of failure events: • The probability of failure Pf can be represented as the probability of falling in the final subset given that on the previous step, the event belonged to subset Fm-1:
Subset Simulation (SS) • By recursively repeating the process, the following equation is obtained: • During the simulation, conditional samples are generated from specially designed Markov Chains so that they gradually populate each intermediate failure region until they cover the whole failure domain. .
Illustration of Subset Simulation Procedure bi are chosen “adaptively” so that the conditional probabilities are approximately to a pre-set value, p0. (e.g. p0=0.1)
Development of Reliability Criteria • Analyze a large number of representative bridge configurations. • Find the reliability indexes for those that have shown good system performance. • Use these reliability index values as criteria for future designs • Find the corresponding deterministic criteria
Input Data for Reliability Analysis • Dead loads • Bending moment resistance: • Composite steel beams • Prestr. concrete beams • Concrete T-beams
Live Load Simulation • Maximum of N events. • 75-yr design life • 5-yr rating cycle • ADTT = 5000 = 1000 = 100 Bin I Bin II Repeat for N loading events
Simulated vs. Measured Single event Two-lane 100-ft span
Maximum Load Effect Max. 5-yr event Two-lane 100-ft span
Reliability-Based Criteria for Bridges • Based on bridge member reliability • Corresponding system safety, redundancy and robustness criteria:
Deterministic Criteria • Ultimate Limit State • Functionality Limit State • Damaged Limit State
Design Criteria • Apply system factor during the design process to reflect level of redundancy • fs <1.0 increases the system reliability of designs with low levels of redundancy. • fs > 1.0 allows members of systems with high redundancy to have lower capacities.
Example Ps/Concrete Bridge 100-ft simple span, 6 beams at 8-ft