Encoding of decision trees for life-cycle cost and decision value analysis via optimization
(2026) In Structural Safety 120.- Abstract
Reliable and cost-effective operation of structural systems over their service life depends on the implementation of Structural Health Monitoring (SHM) and maintenance activities, which influence operational costs, the expected costs of failure and downtime. Decision Value Analysis (DVA) provides a framework to quantify the value of such activities by evaluating their effect on total expected lifecycle costs. While prior studies have focused on isolated decisions or employed heuristic rules to reduce computational demands, an integrated, system-wide, lifetime-based approach is needed to capture interdependencies among components and among decisions, avoiding suboptimal outcomes. This paper addresses the complex problem of optimizing a... (More)
Reliable and cost-effective operation of structural systems over their service life depends on the implementation of Structural Health Monitoring (SHM) and maintenance activities, which influence operational costs, the expected costs of failure and downtime. Decision Value Analysis (DVA) provides a framework to quantify the value of such activities by evaluating their effect on total expected lifecycle costs. While prior studies have focused on isolated decisions or employed heuristic rules to reduce computational demands, an integrated, system-wide, lifetime-based approach is needed to capture interdependencies among components and among decisions, avoiding suboptimal outcomes. This paper addresses the complex problem of optimizing a sequence of SHM and maintenance decisions over the structure's entire service life, without relying on fixed pre-defined heuristic rules. An approach to encode all decision variables into a single vector of design variables is presented, and an adaptive surrogate modeling strategy is employed to efficiently approximate the total expected cost function, significantly reducing the computational burden. A case study on corrosion in buried steel pipelines is presented, allowing up to nine inspections and the associated repair decisions, resulting in 1533 decision variables and 21533 possible combinations. Results indicate, as expected, that early inspections may be omitted when their cost exceeds the marginal benefit in risk reduction, but also that more frequent inspections can support more effective repair decisions. The proposed approach provides a generalizable and computationally efficient framework for lifecycle DVA, which can be directly applied to more complex problems, and is capable of incorporating multiple inspection and maintenance methods.
(Less)
- author
- Gomes, Wellison José de Santana ; Thöns, Sebastian LU and Beck, André Teófilo
- organization
- publishing date
- 2026-05
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Decision analysis, Life-cycle analysis, Structural health monitoring, Surrogate model, Value of information
- in
- Structural Safety
- volume
- 120
- article number
- 102689
- publisher
- Elsevier
- external identifiers
-
- scopus:105027729692
- ISSN
- 0167-4730
- DOI
- 10.1016/j.strusafe.2026.102689
- language
- English
- LU publication?
- yes
- id
- 444b61db-84cc-4fc2-9eac-b0080c7e47b8
- date added to LUP
- 2026-02-18 10:51:26
- date last changed
- 2026-02-18 10:52:05
@article{444b61db-84cc-4fc2-9eac-b0080c7e47b8,
abstract = {{<p>Reliable and cost-effective operation of structural systems over their service life depends on the implementation of Structural Health Monitoring (SHM) and maintenance activities, which influence operational costs, the expected costs of failure and downtime. Decision Value Analysis (DVA) provides a framework to quantify the value of such activities by evaluating their effect on total expected lifecycle costs. While prior studies have focused on isolated decisions or employed heuristic rules to reduce computational demands, an integrated, system-wide, lifetime-based approach is needed to capture interdependencies among components and among decisions, avoiding suboptimal outcomes. This paper addresses the complex problem of optimizing a sequence of SHM and maintenance decisions over the structure's entire service life, without relying on fixed pre-defined heuristic rules. An approach to encode all decision variables into a single vector of design variables is presented, and an adaptive surrogate modeling strategy is employed to efficiently approximate the total expected cost function, significantly reducing the computational burden. A case study on corrosion in buried steel pipelines is presented, allowing up to nine inspections and the associated repair decisions, resulting in 1533 decision variables and 2<sup>1533</sup> possible combinations. Results indicate, as expected, that early inspections may be omitted when their cost exceeds the marginal benefit in risk reduction, but also that more frequent inspections can support more effective repair decisions. The proposed approach provides a generalizable and computationally efficient framework for lifecycle DVA, which can be directly applied to more complex problems, and is capable of incorporating multiple inspection and maintenance methods.</p>}},
author = {{Gomes, Wellison José de Santana and Thöns, Sebastian and Beck, André Teófilo}},
issn = {{0167-4730}},
keywords = {{Decision analysis; Life-cycle analysis; Structural health monitoring; Surrogate model; Value of information}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Structural Safety}},
title = {{Encoding of decision trees for life-cycle cost and decision value analysis via optimization}},
url = {{http://dx.doi.org/10.1016/j.strusafe.2026.102689}},
doi = {{10.1016/j.strusafe.2026.102689}},
volume = {{120}},
year = {{2026}},
}