A probabilistic approach to assess the computational uncertainty of ultimate strength of hull girders

The simplified progressive collapse method is codified in the IACS Common Structural Rules (CSR) to calculate the ultimate strength of ship hull girders in longitudinal bending. Several benchmark studies have demonstrated the uncertainty of this method, which is primarily attributed to the variation in the load-shortening curve (LSC) of local structural components adopted by different participants. Quantifying this computational uncertainty will allow the model error factor applied for the ultimate strength of hull girder in a reliability-based ship structural design to be determined. A probabilistic approach is proposed in this paper to evaluate the prediction uncertainty of ultimate strength of the hull girder caused by the critical characteristics within the LSCs. The probability distributions of critical load-shortening characteristics of stiffened panels are developed based on a dataset generated by empirical formulae and the nonlinear finite element method. An adaptable LSC formulation, with the ability to cater for specific response features of local components, is utilised in conjunction with the Monte-Carlo simulation procedure and the simplified progressive collapse method to calculate the ultimate strength of a hull girder at each sampling. The proposed method is applied to four merchant ships and four naval vessels. The computational uncertainties of the ultimate strength of the case study vessels are discussed in association with their mean values and standard deviations. The study shows that the ultimate strength of ship hull girders is subjected to different uncertainties in sagging and hogging. Whist the strength of merchant ships are primarily governed by the ultimate compressive strength of critical stiffened panels, the strength of naval vessels are also sensitive to the post-collapse response of critical members

Reliability and sensitivity analysis of civil and marine structures using machine-learning-assisted simulation

Civil and marine structures are subjected to various deterioration mechanisms due to aggressive environmental effects or mechanical loads. In order to maintain an acceptable performance level of these structures, previous research have focused on developing methodologies to quantify their reliability and provide optimized management plans that can reduce the life-cycle cost and failure risk. However, the successful implementation of these methodologies is contingent upon the ability to consider various uncertainties associated with structural performance. These include uncertainties associated with environmental and human-induced stressors, as well as those affecting material and geometrical characterization as well as performance prediction models. Monte Carlo simulation (MCS) with a sufficient number of samples can provide accurate quantification of the structural performance under uncertainty. However, for complex problems that require detailed finite element (FE) modeling to predict the system performance, the computational cost can be very high. This problem can be addressed by using advanced sampling techniques that can provide an accurate estimation of the reliability with a significantly lower number of samples. Another approach is to use surrogate models to establish an accurate approximation of the complex system behavior. These models can provide statistically equivalent results of a complex simulation model, with no known closed-form solution, through a limited number of original model executions.The proposed research focuses on developing probabilistic approaches for the performance assessment of civil and marine structures using machine-learning-assisted MCS. In this approach, machine learning is used to generate a surrogate model of the system response and is next integrated into the MCS to quantify the failure probability of the structure. Sensitivity analysis is conducted to identify the key contributing variables that significantly affect the system response. This process helps reduce the number of random variables associated with the problem resulting in a more efficient probabilistic simulation process. The developed approach was applied to solve two major research problems in civil and marine engineering: (a) reliability quantification of eccentrically loaded steel connections employing both welds and bolts for force transfer and (b) characterizing the crack propagation in stiffened panels and quantifying the reliability of ship hulls under realistic loading conditions

Reliability-based Inspection Planning with Application to Deck Structure Thickness Measurement of Corroded Aging Tankers.

Structural inspection is a critical part of the ship structural integrity assessment. Corrosion, as a very pervasive type of structural degradation, can potentially lead to catastrophic failure or unanticipated out-of-service time. In order to mitigate the unfavorable consequences of age-related structural failure, a wisely planned inspection is needed. The current practice of calendar-based inspection of ship structures may cause either an unexpected stoppage during normal routine due to unpredicted structural failures or yield higher costs for unnecessary inspections. Therefore, a strategy to determine timely and effective inspection plans is highly desirable. Probabilistic tools have been used in ship structure analysis for years. Recently, there is revived interest in the reliability-based inspection planning of ship structures. This study is devoted to demonstrating a practical methodology and procedure that adopts a reliability-based approach in structural inspection planning of ship structures. Scheduling a gauging survey for deck panels of oil tankers is used to demonstrate the proposed procedure. This approach includes the derivation of explicit limit state functions for the ultimate strength failure of deck panels based on the equations stated in the International Association of Classification Societies’ Common Structure Rules for double hull oil tanker (2008), and quantifies the various types of uncertainties involved. A time-variant probabilistic corrosion model is derived based on the gauging data collected by the American Bureau of Shipping. Monte Carlo Simulation method with Latin Hypercube Sampling is used for calculating time-variant probability of ultimate strength failure is obtained. By comparing the calculated failure probabilities with the target reliability levels, the inspection intervals can then be determined. The reliability formulations derived in this study are applied to a case study in which the reliability assessment of the deck panels and associated inspection planning of a total of six oil tanker ship designs are carried out. Sensitivity analyses are also performed to investigate the relative contribution of each basic variable. The limitation of the proposed procedure is also discussed along with potential future work.Ph.D.Naval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75877/1/jtguo_1.pd

Optimising non-destructive examination of newbuilding ship hull structures by developing a data-centric risk and reliability framework based on fracture mechanics

This thesis was previously held under moratorium from 18/11/19 to 18/11/21Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure.Ship structures are made of steel members that are joined with welds. Welded connections may contain various imperfections. These imperfections are inherent to this joining technology. Design rules and standards are based on the assumption that welds are made to good a workmanship level. Hence, a ship is inspected during construction to make sure it is reasonably defect-free. However, since 100% inspection coverage is not feasible, only partial inspection has been required by classification societies. Classification societies have developed rules, standards, and guidelines specifying the extent to which inspection should be performed. In this research, a review of rules and standards from classification bodies showed some limitations in current practices. One key limitation is that the rules favour a “one-size-fits-all” approach. In addition to that, a significant discrepancy exists between rules of different classification societies. In this thesis, an innovative framework is proposed, which combines a risk and reliability approach with a statistical sampling scheme achieving targeted and cost-effective inspections. The developed reliability model predicts the failure probability of the structure based on probabilistic fracture mechanics. Various uncertain variables influencing the predictive reliability model are identified, and their effects are considered. The data for two key variables, namely, defect statistics and material toughness are gathered and analysed using appropriate statistical analysis methods. A reliability code is developed based Convolution Integral (CI), which estimates the predictive reliability using the analysed data. Statistical sampling principles are then used to specify the number required NDT checkpoints to achieve a certain statistical confidence about the reliability of structure and the limits set by statistical process control (SPC). The framework allows for updating the predictive reliability estimation of the structure using the inspection findings by employing a Bayesian updating method. The applicability of the framework is clearly demonstrated in a case study structure