Details
| Original language | English |
|---|---|
| Article number | 04024034 |
| Number of pages | 8 |
| Journal | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering |
| Volume | 10 |
| Issue number | 3 |
| Early online date | 29 Apr 2024 |
| Publication status | Published - Sept 2024 |
Abstract
This research describes a novel method for approximating the survival signature for very large systems. In recent years, the survival signature has emerged as a capable tool for the reliability analysis of critical infrastructure systems. In comparison with traditional approaches, it allows for complex modeling of dependencies, common causes of failures, as well as imprecision. However, while it enables the consideration of these effects, as an inherently combinatorial method, the survival signature suffers greatly from the curse of dimensionality. Critical infrastructures typically involve upward of hundreds of nodes. At this scale analytical computation of the survival signature is impossible using current computing capabilities. Instead of performing the full analytical computation of the survival signature, some studies have focused on approximating it using Monte Carlo simulation. While this reduces the numerical demand and allows for larger systems to be analyzed, these approaches will also quickly reach their limits with growing network size and complexity. Here, instead of approximating the full survival signature, we build a surrogate model based on normalized radial basis functions where the data points required to fit the model are approximated by Monte Carlo simulation. The resulting uncertainty from the simulation is then used to build an interval predictor model (IPM) that estimates intervals where the remaining survival signature values are expected to fall. By applying this imprecise survival signature, we can obtain bounds on the reliability. Because a low number of data points is sufficient to build the IPM, this presents a significant reduction in numerical demand and allows for very large systems to be considered.
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Building and Construction
- Engineering(all)
- Safety, Risk, Reliability and Quality
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Vol. 10, No. 3, 04024034, 09.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Interval Predictor Model for the Survival Signature Using Monotone Radial Basis Functions
AU - Behrensdorf, Jasper
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Funding Information: We would like to appreciate the support of the National Natural Science Foundation of China under Grant 72271025.
PY - 2024/9
Y1 - 2024/9
N2 - This research describes a novel method for approximating the survival signature for very large systems. In recent years, the survival signature has emerged as a capable tool for the reliability analysis of critical infrastructure systems. In comparison with traditional approaches, it allows for complex modeling of dependencies, common causes of failures, as well as imprecision. However, while it enables the consideration of these effects, as an inherently combinatorial method, the survival signature suffers greatly from the curse of dimensionality. Critical infrastructures typically involve upward of hundreds of nodes. At this scale analytical computation of the survival signature is impossible using current computing capabilities. Instead of performing the full analytical computation of the survival signature, some studies have focused on approximating it using Monte Carlo simulation. While this reduces the numerical demand and allows for larger systems to be analyzed, these approaches will also quickly reach their limits with growing network size and complexity. Here, instead of approximating the full survival signature, we build a surrogate model based on normalized radial basis functions where the data points required to fit the model are approximated by Monte Carlo simulation. The resulting uncertainty from the simulation is then used to build an interval predictor model (IPM) that estimates intervals where the remaining survival signature values are expected to fall. By applying this imprecise survival signature, we can obtain bounds on the reliability. Because a low number of data points is sufficient to build the IPM, this presents a significant reduction in numerical demand and allows for very large systems to be considered.
AB - This research describes a novel method for approximating the survival signature for very large systems. In recent years, the survival signature has emerged as a capable tool for the reliability analysis of critical infrastructure systems. In comparison with traditional approaches, it allows for complex modeling of dependencies, common causes of failures, as well as imprecision. However, while it enables the consideration of these effects, as an inherently combinatorial method, the survival signature suffers greatly from the curse of dimensionality. Critical infrastructures typically involve upward of hundreds of nodes. At this scale analytical computation of the survival signature is impossible using current computing capabilities. Instead of performing the full analytical computation of the survival signature, some studies have focused on approximating it using Monte Carlo simulation. While this reduces the numerical demand and allows for larger systems to be analyzed, these approaches will also quickly reach their limits with growing network size and complexity. Here, instead of approximating the full survival signature, we build a surrogate model based on normalized radial basis functions where the data points required to fit the model are approximated by Monte Carlo simulation. The resulting uncertainty from the simulation is then used to build an interval predictor model (IPM) that estimates intervals where the remaining survival signature values are expected to fall. By applying this imprecise survival signature, we can obtain bounds on the reliability. Because a low number of data points is sufficient to build the IPM, this presents a significant reduction in numerical demand and allows for very large systems to be considered.
UR - http://www.scopus.com/inward/record.url?scp=85191951709&partnerID=8YFLogxK
U2 - 10.1061/AJRUA6.RUENG-1219
DO - 10.1061/AJRUA6.RUENG-1219
M3 - Article
AN - SCOPUS:85191951709
VL - 10
JO - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
JF - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
SN - 2376-7642
IS - 3
M1 - 04024034
ER -