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Reliability and Risk Analysis of Structures and Applications to Design and Optimization

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 20117

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Dr. Junho Chun

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School of Architecture, Syracuse University, Syracuse, NY 13244, USA
Interests: structural reliability engineering; reliability-based design and topology optimization; random vibration analysis; structural and architectural design integration
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Dr. Won-Hee Kang

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Guest Editor

Centre for Infrastructure Engineering, School of Engineering, Design & Built Environment, Western Sydney University, Penrith, NSW 2751, Australia
Interests: structural system reliability; reliability based design code calibration; system-level stochastic damage detection; disaster risk management; probabilistic strength models
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advancements in structural reliability and risk analysis and their applications in design and optimization. Research developments in structural reliability and risk analysis have accompanied growing attention in sustainability, resilience, and integrity of engineering systems, including structures and infrastructure under uncertainties in research communities and practice. Increasing computational power and efficiency have facilitated the development of diverse methods and simulation techniques for the reliability and risk analysis of complex and complicated systems. This issue is devoted to the latest theoretical and numerical developments in reliability assessment and risk analysis of structures and structural systems.

Reliability analysis and uncertainty quantification techniques are increasingly incorporated in practice, with the integration of multi-objective, multi-scale formulations, high-performance gradient-based/heuristic optimization algorithms, machine learning, and parallel computing for reliable and robust structural design and optimization. The application of these advancements in engineering problems have aided academic researchers and practicing engineers. Therefore, recent research advancements of reliability-based design, engineering, and optimization for the enhancement of the safety and reliability of structures will be highlighted in this Special Issue.

The scope and topics of this issue include, but are not limited to: structural system reliability; methods of reliability and risk assessment of a structure or structural system; reliability modeling and prediction; time-invariant and time-variant reliability and risk analysis; random vibration; machine learning, sensitivity analysis; algorithmic developments in reliability/robust based design optimization; novel applications of structural reliability methods and risk analysis in diverse areas such as structural mechanics, construction material, and design; data-driven uncertainty quantification and risk analysis.

We welcome original research papers, review articles with new insights and perspectives on pioneering developments and their applications, including industrial case studies. Papers submitted for this Special Issue will undergo a rigorous peer-review process for the broad dissemination of the research findings, advancements in technology, and the development of new methodologies and their applications. Authors who are experts in these fields of study are invited and encouraged to submit their contributions to this Special Issue.

Dr. Junho Chun
Dr. Won-Hee Kang
Guest Editors

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Keywords

structural reliability
risk analysis
reliability-based design and optimization
system reliability
complex systems
machine learning
data-driven uncertainty quantification

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Reliability and Risk Analysis of Structures and Applications to Design and Optimization, 2nd Edition in Applied Sciences (1 article)

Published Papers (11 papers)

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Research

Jump to: Review

13 pages, 1540 KiB

Open AccessArticle

NSGA–III–XGBoost-Based Stochastic Reliability Analysis of Deep Soft Rock Tunnel

by Jiancong Xu, Chen Sun and Guorong Rui

Appl. Sci. 2024, 14(5), 2127; https://doi.org/10.3390/app14052127 - 4 Mar 2024

Cited by 3 | Viewed by 1081

Abstract

How to evaluate the reliability of deep soft rock tunnels under high stress is a very important problem to be solved. In this paper, we proposed a practical stochastic reliability method based on the third-generation non-dominated sorting genetic algorithm (NSGA–III) and eXtreme Gradient Boosting (XGBoost). The proposed method used the Latin hypercube sampling method to generate the dataset samples of geo-mechanical parameters and adopted XGBoost to establish the model of the nonlinear relationship between displacements and surrounding rock mechanical parameters. And NSGA–III was used to optimize the surrogate model hyper-parameters. Finally, the failure probability was computed by the optimized surrogate model. The proposed approach was firstly implemented in the analysis of a horseshoe-shaped highway tunnel to illustrate the efficiency of the approach. Then, in comparison to the support vector regression method and the back propagation neural network method, the feasibility, validity and advantages of XGBoost were demonstrated for practical problems. Using XGBoost to achieve Monte Carlo simulation, a surrogate solution can be provided for numerical simulation analysis to overcome the time-consuming reliability evaluation of initial support structures in soft rock tunnels. The proposed method can evaluate quickly the large deformation disaster risks of non-circular deep soft rock tunnels. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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25 pages, 10913 KiB

Open AccessArticle

Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures

by Junho Chun

Appl. Sci. 2024, 14(2), 882; https://doi.org/10.3390/app14020882 - 19 Jan 2024

Cited by 2 | Viewed by 1302

Abstract

This study introduces a reliability analysis methodology employing Kriging modeling enriched by a hybrid active learning process. Emphasizing noise integration into structural response predictions, this research presents a framework that combines Kriging modeling with regression to handle noisy data. The framework accommodates either constant variance of noise for all observed responses or varying, uncorrelated noise variances. Hyperparameters and the variance of the Kriging model with noisy data are determined through maximum likelihood estimation to address inherent uncertainties in structural predictions. An adaptive hybrid learning function guides design of experiment (DoE) point identification through an iterative enrichment process. This function strategically targets points near the limit-state approximation, farthest from existing training points, and explores candidate points to maximize the probability of misclassification. The framework’s application is demonstrated through metamodel-based reliability analysis for continuum and discrete structures with relatively large degrees of freedom, employing subset simulations. Numerical examples validate the framework’s effectiveness, highlighting its potential for accurate and efficient reliability assessments in complex structural systems. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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25 pages, 3082 KiB

Open AccessArticle

Construction Risk Assessment of Yellow River Bridges Based on Combined Empowerment Method and Two-Dimensional Cloud Model

by Lei Wang, Ruibao Jin, Jianpeng Zhou and Qingfu Li

Appl. Sci. 2023, 13(19), 10942; https://doi.org/10.3390/app131910942 - 3 Oct 2023

Cited by 3 | Viewed by 1376

Abstract

(1) In recent years, the economy of the Yellow River basin in China has developed rapidly, and a series of large bridges across the Yellow River have been built on both sides of the Yellow River, which has brought great convenience to regional socio-economic activities. However, risk events are prone to occur during the construction of bridges across the Yellow River, which affect the safety of the bridges’ structure. Therefore, it is necessary to establish a new scientific risk assessment system for the construction safety of bridges across the Yellow River. (2) Methods: Firstly, based on the construction safety risk assessment index system of bridges across the Yellow River, the cloud AHP method and the cloud entropy weight method are used to determine the subjective weight and objective weight of risk indexes, and then the game theory combination weighting method is used to determine the comprehensive weight of each risk index, and then the digital characteristic values of the risk probability cloud and the consequence cloud are calculated and input into the forward cloud generator algorithm. MATLAB was used to generate a two-dimensional comprehensive cloud map, which was visually compared with the standard cloud map, and the probability level and consequence level of each risk index were preliminarily obtained. Finally, the risk assessment matrix was used for comprehensive risk evaluation. (3) Results: Applying the method to the construction safety risk assessment of the Jiaoping Expressway, the overall construction safety risk level of the Yellow River bridge was determined as level 4, and the risk levels of the four primary indicators were: personnel risk (level 3), material and equipment risk (level 4), construction technology risk (level 5), and construction environment risk (level 4). (4) Conclusions: The results of the risk evaluation are consistent with the actual construction state of the bridge, which shows that game theory’s combination of empowerment with a two-dimensional cloud model is scientific and effective when applied to the construction safety risk evaluation of Yellow River bridges. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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21 pages, 8607 KiB

Open AccessArticle

Piling Data-Driven Framework for Optimized Pile Structures Based on Minimizing the Expected Total Cost

by Naoki Suzuki and Kohei Nagai

Appl. Sci. 2023, 13(18), 10216; https://doi.org/10.3390/app131810216 - 11 Sep 2023

Viewed by 1304

Abstract

The use of data in the construction industry is growing rapidly. However, projects that do not have multiple stages, such as pile foundation and cantilever wall construction, are difficult to reinforce based on the data of observation. It cannot be said that the design–build construction process is optimized by piling data and active learning. In this paper, a new data-driven framework is proposed so that it can be used even for construction under single-stage conditions. The proposed method adopts a lower safety factor (SF) in the preliminary design than that in the conventional methods, and checks the performance after the building using piling data. Countermeasures are conducted to satisfy the target reliability, if necessary. Focusing on the expected total cost, the parametric studies reveal that the proposed method can reduce the expected total cost under specific conditions, such as lower countermeasure cost, higher failure cost, and higher relative costs of safety measures. Furthermore, our method exhibits robustness, as even with low initial safety factors, the expected total cost does not become excessively larger compared to the conventional methods. The findings highlight the potential benefits of piling data for optimizing construction projects under single-stage conditions. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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29 pages, 11112 KiB

Open AccessArticle

Fragility Analysis of Transmission Towers Subjected to Downburst Winds

by Chao Zhu, Qingshan Yang, Dahai Wang, Guoqing Huang and Shuguo Liang

Appl. Sci. 2023, 13(16), 9167; https://doi.org/10.3390/app13169167 - 11 Aug 2023

Cited by 6 | Viewed by 2125

Abstract

A downburst is a typical local highly intensive wind all over the world, which is attributed to be the main cause of wind damage to transmission lines in inland areas worldwide. The collapse accidents of transmission towers under the downburst still occur every year. Therefore, it is of great significance to assess the safety of the transmission towers under downbursts. The motivation of the present study is to propose a fragility assessment method for transmission towers under the action of a downburst considering the uncertainty of wind-resistance capacity and the stochastic wind load effect. First, the downburst wind field of the transmission tower with different wind attack angles and different radial distances is simulated according to the mixed stochastic model. Then, random material characteristic samples are generated by the Latin hypercube sampling technique and applied to establish uncertain finite element models for transmission towers. Next, the static nonlinear buckling analysis is carried out by numerical methods to determine the ultimate capacity under the downburst wind load. The parameter analysis of different wind attack angles and radial distances between the downburst and the tower is conducted to determine the most unfavorable location of the maximum response. The failure mode of the transmission tower and the probabilities of the initial failure main members are summarized. Finally, the fragility curves of the transmission tower under the downburst and the atmospheric boundary layer (ABL) wind are compared. The results show that the maximum response is located at R = 1.6D. Most of the initial buckling members are located close to the first section of the tower. The fragility curves of the tower under the downburst are more dangerous than the ABL wind with the attack angle increasing from 0° to 90°. Furthermore, considering the probability model of intensity and direction of the downburst and based on the previous fragility analysis, the collapse probability of the transmission tower caused by the downburst is obtained. By probability analysis of the parameters, including layout conditions, different directions, and different wind speeds, it is found that the most favorable arrangement is 157.5°, and the most unfavorable arrangement is 112.5°. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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19 pages, 2793 KiB

Open AccessArticle

Using System Reliability Concepts to Derive Partial Safety Factors for Punching Shear with Shear Reinforcement: An Explorative Analysis

by Tânia Feiri, Jan Philip Schulze-Ardey, Marcus Ricker and Josef Hegger

Appl. Sci. 2023, 13(3), 1360; https://doi.org/10.3390/app13031360 - 19 Jan 2023

Cited by 2 | Viewed by 1602

Abstract

Punching shear-reinforced flat slabs can fail in three modes: (1) inside the shear-reinforced zone, (2) outside the shear-reinforced zone, or (3) at the maximum punching shear capacity. By using the theory of structural systems reliability, this study investigates how these failure modes combined affect the safety level of reinforced concrete flat slabs with punching shear reinforcement, and ultimately, the variation of partial factors. Based on DIN EN 1992-1-1 together with the German National Annex DIN EN 1992-1-1+NA(D), and by using distinct reliability-based methods (i.e., levels II and III), the variation in the system failure probabilities and, consequently, partial factors were analysed. The results indicated that partial factors can fluctuate significantly depending on the governing ultimate limit state function and the scatter of basic variables. Furthermore, they indicated that the chosen reliability-based method had a significant influence on the resulting safety level. The sensitivity analysis confirmed the premise that partial factors can be reduced without compromising socially accepted safety levels (e.g., recommended in DIN EN 1990). Ultimate benefits include material and cost savings and CO₂ emission reductions. Finally, the approach addressed in this paper offers a new perspective on the derivation of partial factors that can be considered by engineering practitioners. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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20 pages, 2394 KiB

Open AccessArticle

Combined Optimization of Friction-Based Isolators in Liquid Storage Tanks

by Alexandros Tsipianitis, Andreas Spachis and Yiannis Tsompanakis

Appl. Sci. 2022, 12(19), 9879; https://doi.org/10.3390/app12199879 - 30 Sep 2022

Cited by 3 | Viewed by 1893

Abstract

Large-scale tanks are widely used for storing chemicals and fuels. Their failure due to natural (e.g., earthquakes) and/or man-made hazards can lead to disastrous consequences. Nonetheless, they are often constructed in seismic-prone regions. For this reason, base isolation is often used for the seismic protection of large tanks, aiming to “decouple” the superstructure from the imposed ground motions. In this study, a combined optimization formulation is presented in order to further improve the seismic response of a base-isolated tank. The main aim is to optimize both the critical design parameters and the placement of the minimum number of isolators at the base of the tank. In particular, a Cuckoo Search (CS) optimizer is used to optimize the dynamic performance of liquid storage tanks, isolated either via single friction pendulum bearings (SFPB) or triple friction pendulum bearings (TFPB). The main objective is to minimize the eccentricity between the center of mass and the center of rigidity of the isolation system, while appropriate constraints are also imposed. Several cases are examined, while the results are compared with respect to isolator displacement fragility curves, as well as the reduced accelerations at the base of the tank. According to the findings of this study, the tank industry can significantly benefit from the proposed approach, as a more cost-efficient design of the base-isolation system of large-scale tanks can be achieved, i.e., using fewer isolators with optimal key parameters. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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21 pages, 8993 KiB

Open AccessArticle

Finite Element Parametric Analysis of High-Strength Eccentrically Braced Steel Frame with Variable-Cross-Section Replaceable Link

by Xiaolei Li, Bin Fan, Shen Li, Gang Liang and Hong Xi

Appl. Sci. 2022, 12(19), 9447; https://doi.org/10.3390/app12199447 - 21 Sep 2022

Viewed by 1829

Abstract

This paper proposes a new type of variable-cross-section replaceable link that can isolate the plastic deformation. The link and the frame beam connect with the expanding section by a bolt along the flange and web separately. It is easy to design an elastic bolt, reduce bolt slippage, and facilitate link replacement after an earthquake. Considering the large elastic deformation range of high-strength steel and the superior plastic deformation of the new type of replaceable link, a high-strength eccentrically braced steel frame with a variable-section replaceable link is raised by setting a new type of variable-section replaceable link in the eccentrically braced steel frame. Then, the existing end plate connection replaceable link test specimen is simulated and verified by using ABAQUS software. The finite element model of the high-strength eccentrically braced steel frame with a variable-cross-section replaceable link is established, and the bearing capacity, stiffness, plastic rotation, plastic distribution, and other bearing mechanisms of the structure are studied by cyclic loading. The length of the energy-consuming region (e), the steel strength of the link and other components, and the length of the replaceable link (e’) are compared and analyzed with regard to the seismic performance of the structure. The results are of great significance for understanding and exploring the force mechanism, energy dissipation characteristics of the new variable-section replaceable link, and the seismic performance of the high-strength eccentrically braced steel frame, and it also provides a reference for subsequent research. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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23 pages, 3294 KiB

Open AccessArticle

Safety Risk Assessment of Highway Bridge Construction Based on Cloud Entropy Power Method

by Qingfu Li, Jianpeng Zhou and Jinghe Feng

Appl. Sci. 2022, 12(17), 8692; https://doi.org/10.3390/app12178692 - 30 Aug 2022

Cited by 15 | Viewed by 2943

Abstract

(1) In recent years, with China’s increasing investment in the transportation industry, the construction of highways and bridges has flourished, bringing great convenience to people’s lives. At the same time, there are many uncertain factors in the process of bridge construction, being prone to construction risks. In order to meet the requirements of sustainable development, it is necessary to accurately evaluate the safety risk level of bridge construction. Therefore, it is necessary to establish a new scientific safety risk evaluation system for highway bridge construction. (2) Methods. Based on the relevant standards and specifications, this paper establishes a highway bridge construction safety risk evaluation index system, and then uses the cloud entropy weight method to objectively weight each risk index, using cloud model theory to conduct a risk assessment, and through the cloud model images directly determine the overall risk level of bridge construction, and the level of risk indicators. (3) Results. Applying this method to the construction safety risk assessment of a particular bridge, the overall construction risk level of the bridge is obtained as “level 4”, and the risk levels of the four first-level indicators are also all “level 4”. (4) Conclusions. The cloud entropy weight method proposed in this paper and the traditional AHP-Extenics method are applied to a bridge construction safety risk evaluation, and the evaluation results obtained are consistent. However, this paper uses the cloud model to improve the entropy weight method in order to calculate the weights, which fully reflects the objectivity of the assignment. The cloud model is used for evaluation, and the risk level of indicators can be determined visually with images. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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17 pages, 3490 KiB

Open AccessArticle

Dimensional Reduction-Based Moment Model for Probabilistic Slope Stability Analysis

by Meng Wang, Ziguang He and Hongbo Zhao

Appl. Sci. 2022, 12(9), 4511; https://doi.org/10.3390/app12094511 - 29 Apr 2022

Cited by 1 | Viewed by 1517

Abstract

Uncertainty is an inevitable factor that influences the function analysis, design, and safe operation in engineering systems. Due to the complexity property and unclear failure mechanism, uncertainty is an intrinsic property of slope engineering. Hence, stability analysis and design cannot meet the demands of slope engineering based on the traditional deterministic method, which cannot deal with uncertainty. In this study, a practical reliability approach was developed to consider the uncertainty factor in slope stability analysis by combining the multiplicative dimensional reduction method (MDRM) and first-order second moment (FOSM). MDRM was used to approximate the complex, nonlinear, high-dimensional, and implicit limit state function. The statistical moment of safety factor was estimated based on the moment method using MDRM. FOSM is adopted to compute the reliability index based on the statistical moment of the safety factor. The proposed method was illustrated and verified by an infinite slope with an analytical solution. The reliability index and failure probability were compared with Monte Carlo simulations (MCS) in various cases. Then, it was applied to a slope based on numerical solutions. The results show that the proposed method is feasible and effective for probabilistic slope stability analysis. The reliability index obtained from the proposed method shows high consensus with the traditional response surface method (RSM). It shows that the proposed method is effective, efficient, and accurate. MDRM provides a practical, simple, and efficient probabilistic slope stability analysis approach. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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Review

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19 pages, 422 KiB

Open AccessReview

A Review on Local Failure Probability Sensitivity Analysis

by Marie Chiron, Jérôme Morio and Sylvain Dubreuil

Appl. Sci. 2023, 13(21), 12021; https://doi.org/10.3390/app132112021 - 3 Nov 2023

Viewed by 912

Abstract

When assessing the reliability of a system, a mathematical model is often defined to replicate the system’s behavior. The inputs of the system are then gathered into two categories, random inputs and deterministic inputs. The failure of the system depends on both categories and here we focus on the influence of the deterministic inputs. Local failure probability sensitivity analysis consists in computing the derivatives of the failure probability with respect to these deterministic parameters and is a fundamental step in reliability-based design optimization. These sensitivities also provide valuable insights into how specific model parameters affect the failure probability, allowing engineers and designers to make informed decisions about adjusting those parameters to enhance reliability. This article explores various techniques developed in the literature for assessing the sensitivity of failure probability with respect to distribution or design parameters. Depending on the nature of the deterministic parameters and the selected input space, different methods are available. The statistical characteristics of the resulting estimates as well as their computational cost are discussed here, for comparison purpose. Full article

(This article belongs to the Special Issue Reliability and Risk Analysis of Structures and Applications to Design and Optimization)

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