Structural Reliability Prediction Research Articles

Intelligent Assessment Method of Structural Reliability Driven by Carrying Capacity Sustainable Target: Taking Bearing Capacity as Criterion

Large-scale building structures are subject to numerous uncertain loads during their service life, leading to a decrease in structural reliability. Real-time analysis and accurate prediction of structural reliability is a key step to improve the bearing capacity of buildings. This study proposes an intelligent assessment method for structural reliability driven by a sustainability target, which incorporated digital twin technology to establish an intelligent evaluation framework for structural reliability. Under the guidance of the evaluation framework, the establishment method of a structural high-fidelity twin model is formed. The mechanical properties and reliability analysis mechanism are established based on the high-fidelity twin model. The theoretical method was validated by experimental analysis of a rigid cable truss construction. The results showed the simulation accuracy of the high-fidelity twin model formed by the modeling method is up to 95%. With the guidance of the proposed evaluation method, the mechanical response of the structure under different load cases was accurately analyzed, and the coupling relationship between component failure and reliability indicators was obtained. The twinning model can be used to analyze the reliability of the structure in real time and help to set maintenance measures of structural safety. By analyzing the bearing capacity and reliability index of the structure, the safety of the structure under load is guaranteed. The sustainability of structural performance is achieved during the normal service period of the structure. The proposed reliability assessment method provides a new approach to improving the sustainability of building bearing capacity.

Features of structural assessment of long life mine shafts

The issues of restoration and reconstruction of mine shafts become of increasingly higher concern these days. Timely accomplishment and complex safety are the first-order conditions of the successful project implementation. The existing regulatory documents in this sphere are inexhaustive. In this respect, and integrated approach is proposed, including geophysical investigation of shaft siding areas, experimental estimation of load-bearing capacity of support systems, experimental computations using 3D finite element method, as well as the analysis and prediction of structural reliability. The authors exemplify implementation of the complex approach. The GRP measurements and seismic research in some shafts revealed long intervals of fractured rocks and damaged rock/lining interface zones, as well as local voids in the space behind the lining. These data were used to adjust mathematical models with the identification of local zones in the fractured shaft side rock mass with low deformation characteristics, and some series of calculations were performed. In the test area of a mine shaft, the calculations and the in-situ measurements of stresses and deformations agreed sufficiently. The authors highlight that the research-based data array enables better reasoned decision-making on design, construction and technology at integrated safety and enhanced efficiency of operations. Moreover, understanding of the actual condition of the shaft lining and adjacent rock mass can help avoid causeless repair which decreases load-bearing capacity of shaft support down to extreme values in the short run, and continually ends with incidents and large accidents in actual practice.

Dynamic Reliability Prediction of Bridges Based on Decoupled SHM Extreme Stress Data and Improved BDLM

Bridge health monitoring system has produced a huge amount of monitored data (extreme stress data, etc.) in the long-term service periods; how to reasonably predict structural dynamic reliability with these data is one key problem in structural health monitoring (SHM) field. In this paper, considering the coupling, randomness, and time variation of SHM data, firstly, the coupled extreme stress data, which are considered as a time series, are decoupled into high-frequency and low-frequency data with the moving average method. Secondly, Bayesian dynamic linear models (BDLM) without priori monitoring error data (e.g., unknown monitored error variance) are built to dynamically predict the decoupled extreme stress; furthermore, the dynamic reliability of bridge members is predicted with the built BDLM and first-order second moment (FOSM) reliability method. Finally, an actual example is provided to illustrate the feasibility and application of the proposed models and methods. The research results of this paper will provide the theoretical foundations for structural reliability prediction.

Prediction of Structural Reliability Through an Alternative Variability-Based Methodology

Abstract The often-competing goals of optimization and reliability design amplify the importance of verification, validation, and uncertainty quantification (VVUQ) to achieve sufficient reliability. Evaluation of a system's reliability presents practical challenges given the large number of permutations of conditions that may exist over the system's operational lifecycle. Uncertainty and variability sources are not always well defined and are sometimes not possible to predict, yielding traditional uncertainty quantification (UQ) techniques insufficient. A variability-based method is proposed to bridge this gap in state-of-the-art UQ practice where sources of uncertainty and variability cannot be readily quantified. At the point of incipient structural failure, the structural response becomes highly variable and sensitive to minor perturbations in conditions. This characteristic provides a powerful opportunity to determine the critical failure conditions and to assess the resulting structural reliability through an alternative variability-based method. Nonhierarchical clustering, proximity analysis, and the use of stability indicators are combined to identify the loci of conditions that lead to a rapid evolution of the response toward a failure condition. The method's utility is demonstrated through its application to a simple nonlinear dynamic single-degree-of-freedom structural model. In addition to the L2 norm, a new stability indicator is proposed called the “instability index,” which is a function of both the L2 norm and the calculated proximity to adjacent loci of conditions with differing structural response. The instability index provides a rapidly achieved quantitative measure of the relative stability of the system for all possible loci of conditions.

Reliability assessment of thick high strength pipelines with corrosion defects

This paper assesses the reliability of thick, high strength corroded pipelines subjected to internal pressure by first order reliability algorithms and Monte Carlo simulation methods. First, the predictions of different burst strength models, including a new prediction model for thick high strength pipelines are compared with experimental results. Model uncertainty factors are derived for intact and corroded pipelines to calibrate and introduce the uncertainties on the models used in the prediction of structural reliability. The uncertainty associated with model uncertainty factors is addressed and the best fitting distribution is identified. Extensive reliability and sensitivity analyses are carried out on intact and corroded pipes with calibrated burst strength models. Through sensitivity analyses performed for increasing levels of corrosion defects, the influence of basic parameters on the burst strength of corroded pipelines is characterized. The results demonstrate that the model uncertainty factors and the depth of corrosion are relatively important variables for corroded pipelines. The results obtained through reliability assessment are compared with that obtained by the strength model proposed in the RAMPIPE guidelines.

Nowa metoda obliczeniowa oceny niezawodności konstrukcji w oparciu o Big Data

A new computational method for structural reliability based on big data is proposed in this paper. Firstly, the big data is collected via structural monitoring and is analyzed. The big data is then classified into different groups according to the regularities of distribution of the data. In this paper, the stress responses of a suspension bridge due to different types of vehicle are obtained. Secondly, structural reliability prediction model is established using the stress-strength interference theory under the repeated loads after the stress responses and structural strength have been comprehensively considered. In addition, structural reliability index is calculated using the first order second moment method under vehicle loads that are obeying the normal distribution. The minimum reliability among various types of stress responses is chosen as the structural reliability. Finally, the proposed method has been validated for its feasibility and effectiveness by an example.

Use of monitored daily extreme stress data for performance prediction of steel bridges: Dynamic linear models and Gaussian mixed particle filter

Abstract Sensors of modern bridge monitoring systems provide a huge amount of data used for reliability prediction. The proper handling of these data is one of the main difficulties in the field of structural health monitoring. To reasonably predict structural time-variant reliability based on the monitored daily extreme stress data, the objectives of this paper are to present: (a) a procedure for the effective incorporation of monitored daily extreme stress data for dynamic reliability prediction of bridge components, (b) a modelling approach of dynamic linear models based on historical daily extreme stress data in structural reliability prediction, and (c) an effective use of Gaussian mixed particle filter combining the monitored daily extreme stress data and dynamic linear models for dynamically predicting structural reliability. The monitored data obtained from an existing bridge is provided to illustrate the feasibility and application of the procedures and models proposed by this paper.

Gaussian copula–Bayesian dynamic linear model–based time-dependent reliability prediction of bridge structures considering nonlinear correlation between failure modes

Bridge structures in service are subjected to long-term ambient environments and continuously increasing traffic demands; therefore, the physical quantities of the existing bridge structures are subjected to changes in both time and space. Through health monitoring for bridges, the data of the load effects of bridge structures, including strain, stress, deflection, and so on, of the specified structural components or structures, can be obtained. The novel monitoring systems installed in bridge structures contain sensors providing a large amount of monitored data. Proper processing of the continuously provided monitored data is one of the main difficulties in the field of structural health monitoring for time-dependent reliability prediction of structural components and/or structures. Under the actions of the common random sources of the time-dependent input load data, the time-dependent nonlinear correlation will exist among the time-dependent output load effect data. The Bayesian dynamic linear models are introduced to predict the time-dependent output variables and model the time-dependent nonlinear correlation coefficients between them. Then, the Gaussian copula–Bayesian dynamic linear models are built based on the Gaussian copula theory and the time-dependent correlation coefficients. The models can better and more feasibly predict the future reliability of bridge structures. Finally, an actual application example is provided to illustrate the feasibility and application of the built Gaussian copula–Bayesian dynamic linear models for structural reliability prediction.

Computing in Civil Engineering—Lessons Learned from the 2007 ASCE International Workshop on Computing in Civil Engineering

On July 24–27, 2007, a group of approximately 150 researchers and practitioners met in Pittsburgh for the 2007 ASCE International Workshop on Computing in Civil Engineering to present and learn about current research and developments on IT/AIS: Information Technology Support to Advance Infrastructure Systems Management. The conference was geared toward academics and professionals who are interested in computing in a wide range of civil engineering research areas, such as sensing and sensor systems, mobile/wearable computing, data modeling, management, mining, and life-cycle assessment and sustainable infrastructure. The three keynote speakers presented fascinating and forwardlooking talks that provided insights on the future directions of the major conference topics. Dr. Steven B. Chase, the chief scientist of the Federal Highway Administration, talked about the role of information technology in the management of an aging highway infrastructure. He introduced the limitations in the data collection practice of bridge inspection and bridge management, and particularly in visual inspections. He then presented example applications of smart sensing technologies that help create smarter bridges. Dr. Dan M. Frangopol, the Fazlur R. Khan Endowed Chair of Structural Engineering and Architecture at Lehigh University, introduced a method for the integration of monitoring and maintenance management of civil infrastructure systems under uncertainty from a life-cycle perspective. He explained the importance of maintaining a satisfactory long-term performance not only of individual bridges, but also of the overall highway network. He presented a novel analytical and computational framework for network-level bridge maintenance management using optimization. This framework integrates time-dependent structural reliability prediction, highway network performance assessment, and life-cycle cost analysis by prioritizing the maintenance resources to deteriorating bridges through simultaneous and balanced minimization of three objective functions: maintenance cost, bridge failure cost, and user cost. The resulting multiobjective optimization problem is solved by a genetic algorithm. Dr. Edward J. Jaselskis, former program director at the National Science Foundation responsible for managing the information technology and infrastructure systems program, talked about

Optimizing Bridge Network Maintenance Management under Uncertainty with Conflicting Criteria: Life-Cycle Maintenance, Failure, and User Costs

During the past decade, a variety of bridge maintenance management methodologies have been developed to cost-effectively allocate limited budgets to deteriorating highway bridges for performance enhancement and lifespan extension. In most existing research and practice, however, bridges are treated individually rather than collectively as integral parts of a transportation network. In addition to safety issues, failure of bridges renders a highway network inaccessible, either partially or completely. This may lead to considerable economic consequences, ranging from agency costs caused by repair/reconstruction to user costs as a result of disrupted network service (e.g., congestion and detour). Therefore, it is both natural and important to maintain the satisfactory long-term performance of not only individual bridges but the highway network. In this paper, a novel analytical/computational framework for network-level bridge maintenance management using optimization is presented. This framework integrates time-dependent structural reliability prediction, highway network performance assessment, and life-cycle cost analysis. Failure occurrences of individual bridges and their effects on the overall performance of the highway network are evaluated probabilistically. The maintenance resources are prioritized to deteriorating bridges through simultaneous and balanced minimization of three objective functions, i.e., maintenance cost, bridge failure cost, and user cost. Each of these cost metrics is computed as the present value of the expected economic expenditure accrued over the specified time horizon. The resulting multiobjective optimization problem is solved by a genetic algorithm. An application example is provided for maintenance management of deteriorating reinforced concrete deck slabs of an existing bridge highway network in Colorado. It is shown that the proposed methodology is effective for enhancing the bridge maintenance management practice at network-level through probabilistic quantification and preferred balance of various life-cycle costs.

Reliability, Brittleness, Covert Understrength Factors, and Fringe Formulas in Concrete Design Codes

The paper analyzes the reliability consequences of the fact that the current design codes for concrete structure contain covert (or hidden) understrength (or capacity reduction) factors. This prevents distinguishing between different combinations of separate risks due to the statistical scatter of material properties, the error of the design formula, and the degree of brittleness of failure mode, and also makes any prediction of structural reliability (or survival probability) impossible. The covert formula error factor is implied by the fact that the design formula was calibrated to pass not through the mean but through the fringe (or periphery, margin) of the supporting experimental data. The covert material randomness factor is the ratio of the reduced concrete strength required for design to the mean of the strength tests. As a remedy, the covert understrength factor of design formula should be made overt, its coefficient of variation (based on the supporting test data) should be specified, and the type of probability distribution (e.g., Gaussian or Weibull) indicated (which then also implies the probability cutoff). Alternatively, the code could give the mean formula, specify its coefficient of variation and type of distribution, and either prescribe the probability cutoff or overtly declare the understrength factor. The mean of strength tests required for quality control should be figured out from the required design strength on the basis of a specified probability cutoff and the coefficient of variation of these tests. Furthermore, it is proposed that the currently used empirical understrength factor, which accounts mainly for the risks of structural brittleness (or lack of ductility), should be based on the expected maximum kinetic energy that could be imparted to the structure. The reliability integral taking into account the randomness of both the load and structural resistance is generalized for the case of multiple (statistically independent) understrength factors. Finally, it is pointed out that the currently assumed proportionality of the tensile and shear strengths to the square root of compressive strength of concrete is realistic only for the mean, but grossly underestimates the scatter of tensile and shear strengths.

A study of stochastic fatigue crack growth modeling through experimental data

To capture the statistical nature of fatigue crack growth, many stochastic models have been proposed in the literature. These models may have been verified by only one data set, and therefore not appreciated by other fellow researchers. Part of the reason is the difficulty and time-consuming in obtaining the statistically meaningful fatigue crack growth data. In the present study, experimental work is carried out to obtain the fatigue crack growth data of a batch of 2024-T351 aluminum alloy specimens. A rather universal stochastic fatigue crack growth model proposed by Yang and Manning is employed to analyze the data. The solution of the stochastic differential equation associated with the stochastic model gives us the crack exceedance probability as well as the probability of random time to reach a specified crack size. Through comparison between the analytical and experimental results, it is found the model with a minor modification can fit the experimental data rather well. Once the appropriate stochastic model is established, it can be used for the fatigue reliability prediction of structures made of the tested material. In the present study, in particular, it can be used for the reliability assessment of aging aircraft made of 2024-T351 aluminum alloy.

Kalman filter based 3d-stochastic inverse boundary element method for flaw identification and structural reliability prediction

A stochastic inverse boundary element method (SIEM) is developed for determination of three-dimensional (3D) flaws, stochastic boundary traction (load) and structural reliability parameters. SIBEM includes the direct solution of stochastic boundary element (SBEM) and the inverse analysis. Minimizing the difference of the initial guess and the objective values by Kalman filtering algorithm, makes it possible to predict the stochastic boundary loads, as well as to determinate the defects and the structural reliability. Two numerial examples, which show the effectiveness of this paper, including the determination of unknown defects and unknown parameter distribution of random boundary traction, are presented and discussed.

Probabilistic and possibilistic models of uncertainty in structural dynamics

The prediction of structural reliability under static loading has received much attention in the field of civil and structural engineering, and well-established methods such as FORM and SORM are available to estimate the failure probability. If the quality and quantity of the available data do not justify a probabilistic analysis, then the reliability can alternatively be assessed by using less precise ‘‘possibilistic’’ methods such as interval analysis or convex modeling. None of these probabilistic or possibilistic methods has as yet been applied extensively to noise and vibration problems, and the present work considers this issue. Initially it is shown that the probabilistic and possibilistic algorithms can all be expressed in the form of a constrained optimization problem, and the methods are then applied to a vibration problem involving a beam with an uncertain mass distribution. The beam is modeled both experimentally and theoretically to highlight various types of uncertainty that can arise: in addition to ‘‘controlled’’ uncertainties involving adjustable lumped mass positions, there are ‘‘uncontrolled’’ uncertainties arising from items such as boundary conditions and the support stiffnesses. The relative merits of the various reliability assessment techniques are discussed in light of this example.

Review of corrosion studies on aluminium metal matrix composites: Turnbull, A. National Physical Laboratory (UK) Report No DMM (A) 15 Nov. 1990 27 pp

Silicon carbide whisker or particulate reinforced aluminum metal matrix composites, especially attractive because of their superior strength, stiffness, low cycle fatigue properties, corrosion fatigue behavior, creep resistance and wear resistance compared with the corresponding wrought aluminum alloys, have shown promise for various critical structural applications. The major drawback of these materials, however, is their less than ideal ductility, fracture toughness and fatigue crack growth rate (FCGR) properties. Consequently, in order to develop a succinct, structural reliability prediction capability that could be easily applied by design engineers to structures manufactured from these metal matrix composites and to examine initial methods whereby the ductility, fracture toughness and crack growth resistance of these materials might be improved, room temperature tensile, fracture toughness and FCGR tests were conducted on 20 v/o SiCw/2124Al (T6), 25 v/o SiCp/6061 Al (F and T6) and 25 v/o SiCw/6061 Al (T6) metal matrix composites. Compared with the corresponding wrought aluminum alloys, the three subject metal matrix composites demonstrated increased yield and ultimate strengths, substantially inferior ductility and fracture toughness, a lower crack propagation resistance and essentially equivalent values of threshold stress intensity range, ΔKth.

Tensile, fracture toughness and fatigue crack growth rate properties of silicon carbide whisker and particulate reinforced aluminum metal matrix composites

Silicon carbide whisker or particulate reinforced aluminum metal matrix composites, especially attractive because of their superior strength, stiffness, low cycle fatigue properties, corrosion fatigue behavior, creep resistance and wear resistance compared with the corresponding wrought aluminum alloys, have shown promise for various critical structural applications. The major drawback of these materials, however, is their less than ideal ductility, fracture toughness and fatigue crack growth rate (FCGR) properties. Consequently, in order to develop a succinct, structural reliability prediction capability that could be easily applied by design engineers to structures manufactured from these metal matrix composites and to examine initial methods whereby the ductility, fracture toughness and crack growth resistance of these materials might be improved, room temperature tensile, fracture toughness and FCGR tests were conducted on 20 v/o SiCw/2124Al (T6), 25 v/o SiCp/6061 Al (F and T6) and 25 v/o SiCw/6061 Al (T6) metal matrix composites. Compared with the corresponding wrought aluminum alloys, the three subject metal matrix composites demonstrated increased yield and ultimate strengths, substantially inferior ductility and fracture toughness, a lower crack propagation resistance and essentially equivalent values of threshold stress intensity range, Δ K th .