Resilience-Oriented Prioritization of Infrastructure Repairs: A Dependency-Aware Protocol for Flood-Affected Roads, Buildings, Drainage Assets, and Pavements

1. Introduction and Research Question

Flood recovery is not only a problem of damage detection; it is a problem of order. After a flood, transportation links, occupied buildings, and drainage networks may all be impaired at once, while repair resources remain limited. In such conditions, the main policy question is not whether assets should be repaired, but which repairs should be advanced first, which should wait, and which should be coordinated because one intervention unlocks several others. This manuscript addresses that question through a research protocol for resilience-oriented prioritization of infrastructure repairs. The protocol is designed for urban and peri-urban areas where flood exposure varies sharply across short distances and where roads, buildings, storm-sewer components, and pavements are functionally interdependent.

The research question is: how can a decision support protocol rank post-flood repair candidates so that the resulting sequence reduces exposure to repeated flooding, restores essential service quickly, and avoids choosing repairs that consume high resources without commensurate resilience benefit? The answer requires a targeted synthesis rather than a full systematic review. The evidence base used here is intentionally scoped to methods that can be transferred into repair prioritization: geospatial flood-exposure modeling for identifying at-risk assets; maintenance-oriented pavement life-cycle analysis for cost, energy, and carbon tradeoffs; remote inspection and digital-twin methods for condition assessment; phased, dependency-aware decision logic for sequencing; and smart-material research for resilience upgrades that exceed ordinary patch-and-return repair. [1] [2] [3] [4] [5]

The protocol is explicitly conceptual. It proposes variables, an operational ranking rule, a decision sequence, and validation tests, but it does not report completed field trials or claim predictive superiority. Its aim is to provide a method that can be implemented and audited by agencies responsible for post-disaster repairs.

2. Evidence Scope and Synthesis Basis

The evidence used in this manuscript is selective and functional rather than comprehensive. Flood-exposure modeling studies show how geospatial digital twins can estimate depth, extent, duration, and velocity at street scale and can identify at-risk roads, buildings, and storm-sewer elements under scenario-based rainfall conditions. That class of evidence supports the upstream identification of repair candidates in locations where repeated inundation is likely unless repairs are sequenced with drainage improvement or elevation measures. [1]

Pavement life-cycle analysis provides a different but complementary foundation. In those studies, pavement alternatives are compared through embodied energy, embodied carbon, and construction-maintenance cost, showing that material and structural choice affects both economic and environmental burden over the maintenance cycle. For repair prioritization, the relevant implication is that a damaged pavement segment should not be ranked only by traffic importance or visible distress; the agency must also consider whether a repair strategy will impose avoidable energy and carbon burdens over the remaining service period. [2]

Bridge-management literature contributes operational methods for remote inspection, damage detection, 3D reconstruction, big-data handling, and digital-twin synchronization. These methods are transferable because the repair problem depends on how damage states are encoded, how uncertain observations are updated, and how inspection data are passed into a decision system. The bridge literature also warns that some digital-twin systems remain sensitive to connectivity and data quality, which is relevant when flood events disrupt communications and create incomplete asset observations. [3]

Construction automation research offers a phased and dependency-aware logic for implementation. Although that work concerns housing production rather than repair, its main transferable idea is that foundational process enablers should precede downstream actions that depend on them. In repair prioritization, the equivalent proposition is that drainage nodes, access roads, and critical utilities may need to be restored before less dependent assets can be effectively repaired or used. [4]

Smart-material research contributes a selection principle rather than a repair method. The relevant point is that some assets can be restored to pre-event condition, while others should be repaired with resilience-enhancing upgrades such as self-sensing layers, self-healing materials, or adaptive components. The literature supports the broader idea that maintenance decisions may be coupled with resilience functions and life-cycle performance, but it does not by itself specify a flood-repair ranking rule. [5]

3. Decision Problem and Asset Classes

The protocol ranks repair candidates drawn from four asset classes: road segments, buildings, storm-sewer assets, and pavement segments. Each candidate i is treated as a repair unit with a pre-event function, a flood-damage state, and a recovery objective. The objective is to maximize resilience gain per constrained unit of repair capacity, where capacity may be expressed in labor-hours, budget, material availability, or crew-days. The protocol is intended for the immediate post-event phase, when the principal task is to determine which repairs should begin first and which should be held until enabling conditions are met.

A road segment is prioritized when its repair restores access to shelters, hospitals, emergency services, or drainage routes, or when it prevents further flood propagation by reopening a blocked flow path. A building is prioritized when occupancy, essential service, or hazard containment is at stake. A storm-sewer asset is prioritized when its failure creates backwater, ponding, or repeated inundation of adjacent assets. A pavement segment is prioritized when it forms a high-traffic route or when repair choices carry materially different energy, carbon, or cost consequences over the maintenance horizon. [1] [2]

The decision unit may be a segment, node, building footprint, or asset subsystem, depending on the available inventory. To avoid mixing scales, the protocol requires that every candidate be mapped to a common repair record containing location, function, observed damage, flood exposure, dependency links, and repair option set. This allows roads and storm sewers to be compared with buildings without pretending that the assets are identical; they are compared only through their role in the recovery network. [3] [1]

4. Variables and Operational Definitions

The proposed ranking uses six core variables. First, exposure severity E_i is the normalized flood burden associated with asset i, derived from modeled or observed maximum depth, duration, and, where relevant, velocity. Second, service criticality C_i captures the importance of the asset for emergency access, occupancy safety, drainage continuity, or network connectivity. Third, damage state D_i expresses the repair need after inspection, on an ordinal scale from minor impairment to functional loss. Fourth, dependency centrality X_i represents the extent to which other repairs depend on completion of asset i. Fifth, repair burden B_i combines projected direct cost, energy demand, and carbon impact for the candidate repair option. Sixth, uncertainty U_i captures incomplete or low-confidence observation of the damage state. [1] [2] [3] [4]

An exact operational rule is required so that the protocol is auditable. Let all variables be normalized to the interval [0,1]. Define the repair priority score as: P_i = wE_i + wC_i + wD_i + wX_i - wB_i - wU_i, where wE + wC + wD + wX + wB + wU = 1. Higher P_i indicates earlier repair. The weights are not fixed constants; they are set through stakeholder calibration, but the protocol requires that wX not be set to zero in assets with known functional dependencies and that wB not be omitted in pavement cases where alternative repair materials differ materially in embodied energy or carbon. [2] [4]

A hypothetical operational threshold may be applied only as a working example. If U_i exceeds a locally defined confidence limit, for example when inspection evidence is fragmentary or conflicting, the candidate is routed to a verification queue rather than ranked directly. This is not a proposed universal cutoff; it is an implementation rule that each jurisdiction must calibrate to its own inspection quality and risk tolerance. [3]

5. Proposed Algorithm for Repair Sequencing

The protocol proceeds in five stages. Stage 1 screens all damaged assets and classifies them by asset type, flood zone, and service role. Stage 2 updates condition data using remote inspection, field reports, sensor feeds, or geospatial damage overlays. Stage 3 constructs the dependency graph G, in which an edge i→j means that repair of i enables, protects, or materially improves repair of j. Stage 4 computes P_i for each candidate and applies dependency rules. Stage 5 allocates limited crews and budgets to a feasible subset of repairs. [3] [4] [1]

The dependency rule is as follows: if asset i is a strict prerequisite for asset j, then j cannot be scheduled before i unless a temporary bypass exists. If two assets are independent, the one with higher P_i is scheduled earlier. If both assets are critical but one has substantially higher B_i, the agency may choose the lower-burden repair first only when the higher-burden asset does not protect a larger number of downstream nodes. This rule is intended to avoid purely local optimization that improves one site while leaving the broader recovery network constrained. [4] [2]

For roads and drainage assets, the algorithm gives special attention to flow corridors. A road segment that also functions as a surface-flow path should be treated as both a mobility asset and a hydraulic interface. When flood modeling indicates that the segment repeatedly accumulates water above a locally defined service threshold, the repair option set must include drainage correction or grade modification, not only surface patching. This is where flood-exposure modeling contributes directly to the repair decision: it identifies whether restoration alone is likely to fail under the next rain event. [1]

For pavements, the protocol compares repair options on equal functional output rather than on construction form alone. If two options restore load-carrying capacity, the one with lower life-cycle burden B_i is preferred, unless the lower-burden option materially increases vulnerability to near-term re-flooding or access loss. This preserves the relevance of energy and carbon accounting without allowing environmental metrics to override critical emergency access. [2] [1]

6. Comparator and Planned Validation Design

The appropriate comparator is the repair order that would arise from a conventional priority rule based mainly on visible damage, reported complaints, or local engineering judgment without explicit dependency analysis or life-cycle burden accounting. The protocol is therefore evaluated against a status quo decision process rather than against an invented benchmark. The primary question is whether the proposed sequence better satisfies resilience and recovery objectives under the same resource limits. [4] [3]

Validation is prospective in design but can be executed retrospectively first. Retrospective replay uses prior flood cases with documented damage maps, inspection records, and realized repair orders. The proposed ranking is generated from the same initial information that would have been available immediately after the flood, then compared with the actual sequence. Comparison measures include agreement in top-ranked assets, correctness of prerequisite ordering, and whether the proposed sequence would have restored critical access sooner under the observed dependency graph. [3] [1]

A second validation layer is scenario testing. The same asset set is re-ranked under alternative flood depths, different crew capacities, and changed weights for cost, carbon, and service criticality. This does not estimate a single universal optimum. It tests whether the ranking is stable under plausible perturbations and whether the protocol behaves sensibly when drainage assets, building repairs, or pavement segments compete for the same resources. [1] [2]

A third validation layer is expert review. Practitioners examine whether the proposed order violates operational logic, ignores access constraints, or places long-horizon efficiency above urgent safety needs. The expert panel does not define the method; it audits whether the method is consistent with post-flood practice. This is especially important where data are incomplete and digital-twin outputs must be interpreted rather than accepted automatically. [3] [4]

7. Failure Cases, Boundary Conditions, and Conflicting Evidence

The protocol is not designed for every recovery context. It is least reliable when flood exposure maps are unavailable, when asset inventories are incomplete, or when the dependency graph is poorly specified. In those cases, the score P_i may appear precise while resting on weak inputs. The method therefore requires explicit uncertainty flags and does not treat incomplete data as if it were complete. [3] [1]

A second boundary condition arises when resilience upgrades and repair urgency conflict. Smart-material literature supports the possibility of self-sensing or self-healing components, but these options may have higher initial burden and may not be appropriate for a rapid emergency repair window. The protocol therefore treats resilience upgrades as conditional options: they are favored when they do not delay essential service restoration beyond a locally accepted limit. The literature supports the concept of multifunctional materials, but it does not authorize automatic substitution of upgraded materials in every repair context. [5]

A third boundary condition concerns environmental and economic metrics. Pavement life-cycle studies indicate that alternative materials can reduce embodied energy, embodied carbon, and cost, yet these indicators are not sufficient for emergency prioritization by themselves. An asset with lower life-cycle burden can still be lower priority if it does not restore access, drainage, or safety. Conversely, a high-burden repair may still be justified when it is the only feasible route to restore a critical network function. This is the main tension the protocol attempts to manage rather than eliminate. [2]

Finally, digital-twin-based rankings can be overconfident if model calibration is weak. Flood models may accurately represent depth and extent yet still underrepresent blockages, local drainage failures, or temporary debris effects. The protocol therefore treats model output as one input among several, not as a substitute for inspection and local knowledge. [1] [3]

8. Prospective Evaluation Protocol and Outcomes

The evaluation framework should measure three outcome families. The first family concerns recovery effectiveness: time to restore access to critical facilities, number of downstream assets enabled by each completed repair, and reduction in the count of assets left functionally isolated. The second family concerns resilience: whether repaired assets remain serviceable under the next plausible flood scenario. The third family concerns burden: cumulative repair cost, energy use, and carbon impact for the selected portfolio. [1] [2]

The main outcome is not a single scalar score but a portfolio comparison. For each tested flood event, the agency records the set of assets repaired under the proposed protocol and under the comparator process. The protocol is preferred if it restores critical functionality earlier, respects prerequisite relationships more consistently, and does not increase repair burden without a resilience gain. Because the method is conceptual, no numerical target is claimed here. Any threshold for acceptable delay, energy use, or carbon increase must be set locally and justified against policy priorities. [4] [2]

A useful secondary outcome is decision traceability. Every ranking decision should be explainable from the recorded variables E_i, C_i, D_i, X_i, B_i, and U_i. Traceability is not a cosmetic feature; it is essential for post-event audit, public accountability, and later revision of weights after a failed or contested repair sequence. The bridge digital-twin literature is especially relevant here because it emphasizes the handling of large, heterogeneous data streams and the need for synchronization between field observations and decision systems. [3]

9. Conclusion

This manuscript establishes a bounded conceptual contribution: a dependency-aware, resilience-oriented protocol for ranking post-flood infrastructure repairs across roads, buildings, storm-sewer assets, and pavements. The protocol integrates flood exposure, service criticality, observed damage, repair dependency, and life-cycle burden into a single auditable sequence for decision-making. It also clarifies when resilience upgrades should be considered and when immediate restoration should take precedence over deeper redesign. [1] [2] [4] [5]

What remains untested is the protocol’s performance in live recovery operations, its stability across different urban morphologies, and its sensitivity to imperfect inventories and uncertain damage reports. Those questions require retrospective replay, scenario analysis, and prospective field validation before the method can be treated as a routine operational tool. The present manuscript therefore offers a precise decision structure, not an empirical claim of superiority. [3] [1] [4]

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