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Posts
Start of Postdoc Research Program
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Excited to share that I’ve started my postdoctoral research at the University of Sao Paulo (USP)! 🎉
portfolio
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publications
A heuristic optimization considering probabilistic constraints via an equivalent single variable Pearson distribution system
Published in Applied Soft Computing, 2019
The computational cost of an optimization considering probabilistic constraints is often expensive due to the reliability analysis performed inside the optimization loop. This study computes reliability using an equivalent single variable Pearson’s distribution system. To retain the accuracy without sacrificing efficiency, dimension reduction method is used to reduce the required number of points in GaussianHermite integration. In addition, four advance machine learning techniques are used to construct a hyper-probability density function to solve the singularity issue in the Pearson distribution system. The ε-constrained is utilized in a metaheuristic optimization algorithm (PSO/SOS) to consider the probabilistic constraints. The proposed algorithm is verified with several literature studies. Results shown that it is able to find an optimal design for problems having linear, highly nonlinear, and implicit probabilistic constraint functions with normal or non-normal variables. It is found that increasing number of variates in dimension reduction can provide a more accurate estimation of moments of the performance functions and enhance the solution accuracy. To demonstrate the applicability of the proposed algorithm to a practical problem, a platform of the SAP2000 Open Application Programming Interface (OAPI) is developed to find an optimal design for a three story steel frame under nonlinear time history analysis.
Recommended citation: Liao, K. W., & Biton, N. I. D. R. (2019). A heuristic optimization considering probabilistic constraints via an equivalent single variable Pearson distribution system. Applied Soft Computing, 78, 670–684. https://doi.org/10.1016/J.ASOC.2019.03.021
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A heuristic moment-based framework for optimization design under uncertainty
Published in Engineering with Computers, 2019
To search an optimal design under uncertainty, this study proposes an effective framework that integrates the moment-based reliability analysis into a heuristic optimization algorithm. Integration of an equivalent single-variable performance function is an ideal concept to calculate the failure probability. However, such integration is often not available and is alternatively computed using the first four moments and a generalized moment-based reliability index is established, in which the Gaussian–Hermite integration and dimension reduction are implemented to enhance the effectiveness. To overcome the limited applicable range of moment-based approach, an adjustable optimization procedure is proposed, in which different reliability methods are performed depending on results of the constraint assessments. In addition, the ε level comparison is integrated into particle-swarm optimization to consider the constraint violation. Several literature studies are used to verify the accuracy of the proposed optimization framework including problems having linear, highly nonlinear, implicit probabilistic constraint functions with normal or non-normal variables and system-level reliability analysis. The effects of several parameters, such as the number of estimate point, the number of dimension, and the degree of uncertainty, are thoroughly investigated. Results indicating that tri-variate with seven points are able to provide a stable solution under a high degree of uncertainty.
Recommended citation: Liao, K.-W., & Biton, N. I. D. (2019). A heuristic moment-based framework for optimization design under uncertainty. Engineering with Computers, 36(4). https://doi.org/10.1007/S00366-019-00759-4
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Probabilistic failure path approach on structural system-reliability-based design optimization of fatigue-induced failure
Published in 14th International Conference on Applications of Statistics and Probability in Civil Engineering, 2023
Reliability analysis with unknown system event definition such as sequence of member failures requires a failure-path approach to determine the component events that will induce overall system failure. In particular, redundant structures prone to fatigue-induced sequential failure needs a system-level analysis employing a failure-path approach to account for stress redistribution. Thus, there can be a high computational cost of incorporating such probabilistic constraints into a SystemReliability-based Design Optimization (SRBDO) framework against fatigue limit states. A structural system reliability analysis procedure, namely, the Branch-and-Bound method employing system reliability Bounds (termed the B3 method) is integrated into an optimization algorithm. A gradientbased optimizer is used to find the optimum, and a modified Sequential Compounding Method (SCM) together with Chun-Song-Paulino (CSP) sensitivity analysis method is used to calculate the gradient with respect to the design variables. Additionally, a new bounding rule of the B3 method is introduced to increase efficiency. To demonstrate the applicability, it is applied to a hypothetical structure of multilayer Daniel’s system. As a result, the system failure probability of the optimal design obtained from the proposed method is found to be lower than the target probability and is verified through Monte Carlo simulation. The calculated gradient of the system failure probability accurately leads to the optimal design. It is confirmed that the proposed method can allocate limited materials throughout the structure. Moreover, the system reliability analysis of fatigue-induced sequential failure is explicitly incorporated into the design optimization, thereby resulting in cost-efficient and safer structures.
Recommended citation: Biton, N. I. D., & Lee, Y.-J. (2023). Probabilistic failure path approach on structural system-reliability-based design optimization of fatigue-induced failure. 14th International Conference on Applications of Statistics and Probability in Civil Engineering. http://www.tara.tcd.ie/handle/2262/103435
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Probabilistic failure path approach on optimal design of structures against sequential fatigue-induced failure
Published in Structural and Multidisciplinary Optimization, 2024
Structural redundancy acts as a safeguard against localized damage, but it may lead to a variety of potential overall failures. A system-level probabilistic failure path approach is necessary to identify system failure events and account for stress redistribution in structures prone to fatigue-induced damage. Incorporating such probabilistic constraints into a SystemReliability-based Design Optimization (SRBDO) framework comes with a high computational cost. In this study, an innovative method integrates the Branch-and Bound method employing system reliability Bounds (B3 method) and modified Sequential Compounding Method (SCM) to compute the gradient of the system failure probability, particularly those requiring a failure path approach like sequential failure. New compounding rules are introduced in SCM: (a) screening and (b) adaptive compounding to enhance accuracy especially for systems with highly correlated events. This approach allows for the utilization of gradient-based optimizers, offering enhanced computational efficiency in comparison to current gradientfree methods. Additionally, a new bounding rule of the B3 method is introduced to further increase efficiency, and ChunSong-Paulino (CSP) sensitivity analysis method is used to calculate the derivatives with respect to the design variables. The proposed method is demonstrated through a hypothetical structure of multilayer Daniel’s system and two truss structures of different scales. The semi-analytical formulation of the sensitivity calculation effectively guides the optimization process to the optimum. This new approach accurately calculates the failure probability of the dominant failure sequences and the overall system failure probability as validated by the Monte Carlo simulation. The numerical studies robustly demonstrated efficiency and accuracy of the proposed optimization framework.
Recommended citation: Biton, N. I., Kang, W.-H., Chun, J., & Lee, Y.-J. (2024). Probabilistic failure path approach on optimal design of structures against sequential fatigue-induced failure. Structural and Multidisciplinary Optimization, 67(11), 199. https://doi.org/10.1007/s00158-024-03918-4
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Structural system reliability-based design optimization considering fatigue limit state
Published in Smart Structures and Systems, 2024
The fatigue-induced sequential failure of a structure having structural redundancy requires system-level analysis to account for stress redistribution. System reliability-based design optimization (SRBDO) for preventing fatigue-initiated structural failure is numerically costly owing to the inclusion of probabilistic constraints. This study incorporates the Branchand-Bound method employing system reliability Bounds (termed the B3 method), a failure-path structural system reliability analysis approach, with a metaheuristic optimization algorithm, namely grey wolf optimization (GWO), to obtain the optimal design of structures under fatigue-induced system failure. To further improve the efficiency of this new optimization framework, an additional bounding rule is proposed in the context of SRBDO against fatigue using the B3 method. To demonstrate the proposed method, it is applied to complex problems, a multilayer Daniels system and a three-dimensional tripod jacket structure. The system failure probability of the optimal design is confirmed to be below the target threshold and verified using Monte Carlo simulation. At earlier stages of the optimization, a smaller number of limit-state function evaluation is required, which increases the efficiency. In addition, the proposed method can allocate limited materials throughout the structure optimally so that the optimally-designed structure has a relatively large number of failure paths with similar failure probability.
Recommended citation: Biton, N. I. D., & Lee, Y.-J. (2024). Structural system reliability-based design optimization considering fatigue limit state. Smart Structures and Systems, 33(3), 177–188. https://doi.org/10.12989/SSS.2024.33.3.177
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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