The comparative analysis of the four integrated maintenance scenarios demonstrates that long\u2011term maintenance planning for multi\u2011component civil infrastructure systems is governed by a combination of operational constraints and lifecycle environmental impacts. Although each scenario introduces a different logic, ranging from access\u2011based grouping to crew\u2011based scheduling, the resulting system behavior reveals consistent patterns that shape both the optimization outcomes and the LCA results.<\/p>\n\n\n\n
Following the analysis results of the four scenarios, cost independence from the interruption duration is shown, and a dependency on the type of maintenance actions performed is reflected. This is clear when reflecting on figures 1.c ,2.c ,3.c and 4.c, as the cost varies significantly between Pareto-optimal solutions. Nevertheless, a relative tendency between all samples can be noted towards a reduced cost associated with lower interruption durations except for the first scenario. Scenarios 1 and 2 have less overall costs for Pareto-optimals than scenarios 3 and 4, almost reaching half of the cost for some cases.<\/p>\n\n\n\n
The lifecycle assessment reveals that environmental indicators (energy, CO\u2082, Nox, SO\u2082) are strongly influenced by the cumulative material demand associated with maintenance activities. Scenarios with more frequent interventions exhibit higher environmental burdens due to repeated material use, even when individual interventions are short and operationally efficient.<\/p>\n\n\n\n
Across all scenarios, the biobjective optimization (minimizing total interruption duration and maximizing minimum spacing between interventions) produces a Pareto front with a similar structure. Two dominant clusters consistently emerge around spacing values of one year and two years, regardless of the underlying strategy. This indicates that the system\u2019s feasible maintenance rhythm is primarily dictated by the inherent frequencies of short\u2011cycle interventions such as surface repairs.<\/p>\n\n\n\n
Scenarios 1 and 2 show a scattered Pareto-optimal performance among the multiple selected criteria, while scenarios 3 and 4 show more balanced performance with a couple of value deviations. This can reflect an unreliability on the first and second integration strategy as it carries more tradeoffs regarding the criteria than the other strategies that may carry a tradeoff in one or more of the high-performance parameters, knowing that scenario 1 can be considered the simplest but it unfolds to being far from perfect in regards of its performance. Meanwhile scenario 3 is leading in its overall performance, only trading the frequency of interventions, which by the way, forms the base counter-logic of scenario 1.<\/p>\n\n\n\n
It is difficult to govern one of these scenarios as the best performing, as they follow different integrated maintenance logic. It is essential to note that these scenarios can be governed by spatial, economical, or social factors. Spatial factors can be represented by the difficulty of accessing the structure due to bad infrastructure, dangerous terrains, or risky weather conditions. Economical factors can control the cost of materials and maintenance-related experts. And social factors represent the absence of expertise in a certain domain needed for effective system maintenance. The domaining situation and surrounding factors should be the leader for choosing the best scenario for a system.<\/p>\n\n\n\n
That being said, and in a conceptual context and judging only by interruption days and distance between consecutive interventions, scenario 4 is considered to be the best option. Nevertheless, scenario 4 focuses primarily on the retaining walls, so this could adversely affect the maintenance of the building, hence affecting its overall performance. Meanwhile, scenarios 1 and 2 have more detailed maintenance actions, providing a more holistic maintenance planning for the integrated system, suggesting that it is more realistic for maintaining complex systems in an overall good condition.<\/p>\n\n\n\n
The four scenarios collectively demonstrate that integrated maintenance planning must be approached as a system-level, multi-objective problem that simultaneously considers operational constraints and lifecycle environmental impacts. Although different organizational strategies introduce varying degrees of flexibility, the fundamental trade-offs between service interruption, intervention spacing, and environmental burden remain stable across scenarios.<\/p>\n\n\n\n
The method used for multi-objective optimization does not weigh different parameters; rather, it treats them equally. NSGA-II fitted our analysis as there are no conflicting objectives, but there are strong tradeoffs. However, using another method that weighs objectives can enhance the decision-making while increasing the computational complexity due to the high dimensionality of our study.<\/p>\n\n\n\n
One factor that undermines our results is that the life cycle inventory on which this study is based is derived from multiple individual reports that use different information resources for similar materials, affecting prices and environmental impact values. This, along with the inclusion of only specific components of the overall system, can reduce the overall accuracy of the demonstrated results.<\/p>\n\n\n\n
Ultimately, the integration of LCA into maintenance optimization provides a more comprehensive understanding of long-term system behavior, embracing decision-makers’ ability to select maintenance strategies that are operationally viable, reflect context-specific needs, and are environmentally responsible.<\/p>\n\n\n\n
Home<\/a> | System Definition<\/a> | Scenario 1<\/a> | Scenario 2<\/a> | Scenario 3<\/a> | Scenario 4<\/a> | Discussion<\/a> | Appendix<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Comparative Results Analysis The comparative analysis of the four integrated maintenance scenarios demonstrates that long\u2011term maintenance planning for multi\u2011component civil infrastructure systems is governed by a combination of operational constraints and lifecycle environmental impacts. AlthoughContinue reading<\/a><\/p>\n","protected":false},"author":284,"featured_media":0,"parent":24763,"menu_order":6,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-24301","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/24301","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/users\/284"}],"replies":[{"embeddable":true,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=24301"}],"version-history":[{"count":17,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/24301\/revisions"}],"predecessor-version":[{"id":26211,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/24301\/revisions\/26211"}],"up":[{"embeddable":true,"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=\/wp\/v2\/pages\/24763"}],"wp:attachment":[{"href":"http:\/\/141.23.68.248\/wp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}