Explainable Models for Maintenance Prioritization in Built-Environment Assets: A Conceptual Framework for Bridges, Buildings, and Pavements

1. Introduction

Maintenance backlogs in built-environment assets are not explained by defect detection alone. Owners must compare unlike risks, different service consequences, unequal budgets, and variable evidence quality across assets that deteriorate differently. Bridges accumulate structural damage under loading and environmental exposure. Buildings can contain safety deficiencies related to circulation, geometry, fixtures, lighting, and occupant vulnerability. Pavements require repeated intervention choices whose cost and environmental implications depend on traffic, materials, and maintenance schedules. A prioritization model is therefore not only a prediction device; it is a decision rule for choosing which action should proceed first when multiple candidate interventions compete for limited resources.

Explainability is central in this setting. Maintenance ranks affect deferral of risk, allocation of public or private funds, and the ability to justify decisions retrospectively. A useful model must therefore reveal why an action is ranked highly, what evidence supports the ranking, and how uncertainty affects confidence in the recommendation. Recent studies provide important fragments of such a model: probabilistic bridge deterioration fusion, explainable building safety assessment from digital twins, explicit pavement maintenance comparison through annualized cost and environmental metrics, and transferable methods for uncertainty-aware modeling and protected distributed learning. [1] [2] [3] [4] [5]

This article develops those strands into a conceptual framework for explainable maintenance prioritization in bridges, buildings, and pavements. The objective is specific: to transform asset-condition evidence into an auditable ranking of candidate maintenance actions under uncertainty while preserving traceability from observation to final decision score.

2. Research question and evidence scope

The research question is: how can an explainable model prioritize maintenance actions across built-environment assets by combining condition evidence, consequence severity, intervention burden, expected action benefit, and uncertainty in a form that remains intelligible to domain experts? This question concerns both modeling and governance because prioritization must support scheduling, escalation of urgent cases, and later audit.

The evidence scope is a targeted narrative synthesis rather than a systematic review. Included studies were selected because they directly support one of five components needed for the proposed framework: bridge deterioration assessment from fused inspection and monitoring data; explainable building inspection from low-cost vision data; pavement maintenance comparison using life-cycle cost, embodied energy, and embodied carbon; explainable uncertainty-aware predictive design; and privacy-preserving, poisoning-robust distributed learning. Work on unrelated forecasting or generic analytics is outside scope because it does not directly inform explainable maintenance prioritization in built assets. [1] [2] [3] [4] [5]

This scope is sufficient for a conceptual contribution but not for a claim that one unified prioritization model already outperforms alternatives in field use. The direct application studies validate their own tasks, not a cross-domain ranking system. The methodological studies are transferable foundations rather than direct tests on bridge, building, and pavement portfolios. [1] [2] [3] [4] [5]

3. Prior evidence and the remaining gap

The bridge study shows that heterogeneous evidence with different temporal resolutions can be integrated in an interpretable probabilistic model. It combines a Weibull-based Accelerated Failure Time formulation for failure risk with a dynamic crack-growth layer estimated from monitoring data, then updates posterior risk as new observations arrive. This is directly relevant because maintenance priorities should respond to both periodic inspections and continuous sensing rather than rely on static inspection snapshots alone. The same study also frames interpretability as necessary under incomplete and censored deterioration observations. [1]

The building study contributes an explainable evidence representation. It reconstructs a metric-scale indoor digital twin from smartphone video, extracts semantic entities and spatial relations, encodes guidelines as computable risk prototypes, and stores evidence in a scene structure that binds geometry, semantics, observations, and regulatory knowledge. Its pilot validation reported high risk recall in some cases but moderate precision and incomplete judgment closure. That mixed result is important for prioritization: detected hazards should not be treated as equally reliable, and unresolved judgments should remain visible in downstream ranking rather than being collapsed into certain labels. [2]

The pavement study provides an explicit burden framework for maintenance planning. It uses defined functional units, construction-maintenance boundaries, and scheduled interventions to compare annualized cost, embodied energy, and embodied carbon. It also states that such sustainability evaluations depend strongly on regional material availability, design practice, traffic, and maintenance strategy. This supports inclusion of life-cycle burden metrics in prioritization while cautioning against direct transfer of coefficients across jurisdictions. [3]

The SGRE study does not address maintenance prioritization directly, but it offers a transferable template for uncertainty-aware explainable prediction. Its combination of adaptive ensemble construction, calibrated prediction intervals, and SHAP-based interpretation supports the broader principle that uncertainty and explanation should be first-order outputs of decision support rather than afterthoughts. [4]

The federated learning study addresses a practical infrastructure problem: condition data are often distributed across owners, facilities, or contractors that cannot pool raw data. It argues that collaborative modeling without moving raw data is feasible, but it also identifies privacy leakage and poisoning as serious risks. Its differentiated privacy allocation, anomaly screening, majority voting, and update clipping are relevant to sensor-rich maintenance settings, while its discussion of the tension between privacy and anomaly detection sets a clear boundary condition for deployment. [5]

What remains missing across these studies is a domain-spanning ranking rule that converts explainable asset-state evidence into ordered maintenance actions. Existing work assesses deterioration, detects hazards, compares life-cycle alternatives, quantifies uncertainty, or secures distributed training. It does not provide a unified, auditable decision sequence for ranking candidate interventions across bridges, buildings, and pavements. [1] [2] [3] [4] [5]

4. Proposed framework: the Explainable Maintenance Prioritization Model

The proposed Explainable Maintenance Prioritization Model (EMPM) is a two-layer decision-support architecture. Layer 1 generates structured condition evidence from inspections, monitoring, or explainable visual assessment. Layer 2 converts that evidence into a ranked list of candidate maintenance actions using a decomposable score and an emergency escalation rule. EMPM is intended for portfolio triage and scheduling support; it does not replace detailed structural design, rehabilitation engineering, or formal code-compliance review.

The decision unit is an action-asset pair a = (i,m), where i denotes a specific asset or asset segment and m denotes a candidate maintenance action such as deck repair, hazard retrofitting, crack sealing, thin overlay, or resurfacing. A pair is scored only if there is a plausible intervention linked to an identified condition state. This avoids ranking assets in the abstract without specifying what action is actually under consideration.

Layer 1 is domain-adaptive. For bridges, the preferred evidence is posterior deterioration risk updated from inspection and monitoring fusion. For buildings, the preferred evidence is a scene-based hazard inventory that links geometric measurements, semantic labels, and rule-based risk prototypes in an explainable digital twin. For pavements, the preferred evidence is an inspection-derived condition state linked to maintenance schedules and annualized burden metrics under declared functional units and boundaries. Where data are distributed across organizations, collaborative model updating may be implemented through federated learning with explicit privacy and poisoning safeguards. [1] [2] [3] [5]

Layer 2 uses five visible components: current condition-risk, consequence criticality, intervention burden, intervention efficiency, and evidence uncertainty. The ranking logic is intentionally simple. Actions should move upward when they address higher risk or more critical consequences and when they are expected to reduce risk efficiently. Actions should move downward when burden is high in absolute terms or when supporting evidence is weak or incomplete. To preserve score stability, EMPM is defined for within-portfolio triage under a fixed scoring protocol. Cross-portfolio comparison is permissible only when the same burden scale, efficiency denominator convention, thresholds, horizon, and action taxonomy are held constant across portfolios. Because the components remain separate, the final ranking can be inspected and challenged rather than accepted as an opaque output.

5. Variables, operational definitions, and assumptions

For each action-asset pair a = (i,m), EMPM defines R_i(H) as the current estimate of maintenance-relevant risk for asset i over a declared decision horizon H, scaled to [0,1]. H must be fixed before scoring for the whole portfolio or for a declared asset class. In bridges, R_i(H) may be the posterior probability that a critical deterioration state will be reached within H, estimated from fused inspection and monitoring data. In buildings, R_i(H) may be a normalized hazard score for the same horizon based on applicable risk prototypes supported by geometric or visual evidence. In pavements, R_i(H) may be a normalized function of condition severity and serviceability deficiency referenced to the same horizon and linked to a maintenance category. The common requirement is ordinal: larger R_i(H) means greater maintenance-relevant risk within H. [1] [2] [3]

Ci is consequence criticality on [0,1]. It represents the impact of non-intervention within the decision horizon and may include structural safety implications, occupant injury exposure, strategic network role, service disruption, or vulnerability of users. EMPM requires each implementing organization to publish a criticality rubric before scoring begins. Asset-class-specific cut-points may then be calibrated locally rather than assumed universal. [2] [3]

Bim is intervention burden. To avoid ambiguity, EMPM defines burden in two stages. First, a raw positive burden quantity bim is assembled for valid candidate actions from direct cost alone or from a weighted combination of direct cost, user disruption, embodied energy, and embodied carbon under a published local protocol. Second, bim is mapped to Bim on [0,1] by a predeclared reference transformation that is fixed before scoring and not re-estimated from the current candidate set. The default rule is min-max scaling against portfolio-program reference bounds established from the organization’s admissible action library for the same asset class and accounting year: Bim = min{1, max{0, (bim - bmin_ref)/(bmax_ref - bmin_ref)}} when bmax_ref > bmin_ref. If the available comparable reference values are fewer than two distinct numbers, so that bmax_ref = bmin_ref, all valid actions receive Bim = 0.5 until the reference library is expanded or a policy replacement scale is declared. Actions with missing or non-comparable burden records are flagged invalid and are not scored until burden is specified. For pavements, annualized cost, embodied energy, and embodied carbon under explicit functional units and maintenance boundaries are directly supported by prior evidence. [3]

Ri_after_m is the predicted post-intervention risk if action m is applied to asset i within the decision horizon. EMPM allows three pathways. Pathway A uses empirical outcome models trained on historical maintenance effects. Pathway B uses engineering scenario models, such as posterior state updating under assumed intervention effects. Pathway C uses a predeclared heuristic delta rule only when empirical and simulation models are unavailable. Under Pathway C, Ri_after_m = max(0, Ri - delta_{class(i),type(m)}), where each delta parameter is specific to an asset class and action type, not to an individual asset. Every delta must be fixed before scoring from one of two admissible sources: published organizational maintenance standards that state an expected condition improvement, or a structured expert elicitation conducted before ranking. In either case, delta values are bounded to [0,1], recorded with justification, and held constant throughout a validation fold. If no justified delta can be declared for an action type, that action is ineligible for scoring under Pathway C.

Intervention efficiency Eim is defined from raw burden rather than normalized burden to avoid the zero-denominator problem created by normalization. The exact rule is Eim_raw = max(0, Ri - Ri_after_m) / max(bim, ε), where ε is a small positive constant chosen before analysis and applied only to valid actions with positive comparable burden records. To avoid dependence on the incidental composition of the current candidate set, Eim_raw is converted to Eim on [0,1] by a fixed reference transformation rather than by rescaling over the current valid actions. The default rule is capped linear scaling against class-specific reference bounds declared in advance: Eim = min{1, max{0, Eim_raw / emax_ref(class(i))}}, where emax_ref(class(i)) > 0 is taken from the same development data or action library used to define burden references. This formulation preserves bounded computation while improving comparability across portfolios scored under the same protocol.

Ui is evidence uncertainty on [0,1], with higher values denoting weaker or less resolved evidence. In bridges, Ui may be informed by posterior interval width, censoring extent, or disagreement between inspection and monitoring signals. In buildings, Ui may reflect incomplete scene coverage, unresolved judgments, low-confidence measurements, or disagreement between geometric and visual checks. In predictive submodels, calibrated prediction intervals or analogous uncertainty outputs are preferred. [1] [2] [4]

The composite priority rule is Priority(a) = w_R R_i(H) + w_C C_i + w_E E_im - w_B B_im - w_U U_i, with all weights non-negative and summing to 1. A separate escalation rule applies before routine ranking: if R_i(H) >= theta_R(class(i)) and C_i >= theta_C(class(i)), then a enters the urgent queue. Urgent actions are ranked only against other urgent actions by the same Priority(a) score; if scores tie, the higher R_i(H) ranks first, then the higher C_i, then the lower U_i. Non-urgent actions are ranked separately and cannot displace urgent actions. Threshold functions are asset-class-specific policy parameters that must be declared in advance. An action is score-valid only if R_i(H), C_i, B_im, Ri_after_m, and U_i are all available under declared measurement rules. Missingness is handled by exclusion, not imputation, in the default protocol: invalid actions are placed in a review queue with the reason for exclusion logged explicitly.

Because burden appears both in E_im and as the explicit penalty term B_im, EMPM treats the relation between w_E and w_B as a governance choice rather than a mathematically guaranteed correction for redundancy. The default operational rule is w_E <= w_B. Weight selection then proceeds in two stages. First, a candidate non-negative weight vector is proposed on the simplex sum w = 1. Second, if the proposal violates w_E <= w_B, it is invalid and must be revised before scoring; EMPM does not auto-project infeasible user-supplied weights. For prospective calibration, the default search space is the feasible simplex under w_E <= w_B, explored by a predeclared grid with fixed increment size, with all evaluated vectors already normalized to sum to 1. The selected vector is the feasible weight set that minimizes mean absolute rank deviation from the expert reference on the training portfolios; ties are broken by choosing the smaller w_E and then the smaller w_B. Sensitivity analysis should then vary each non-zero weight upward and downward within the feasible simplex while holding the others proportionally rescaled.

6. Algorithm and decision sequence

The EMPM decision sequence contains eight steps. Step 1 defines the portfolio, decision horizon, asset classes, admissible actions, and whether ranking is portfolio-wide or segmented. Step 2 assembles evidence from inspections, monitoring streams, or explainable visual scene analysis. Step 3 converts raw observations into structured variables, such as posterior bridge deterioration risk, building hazard prototypes with measurement traces, or pavement condition linked to maintenance schedules and burden metrics. Step 4 estimates R_i(H), C_i, b_im, B_im, Ri_after_m, E_im, and U_i for each action-asset pair and assigns any pair with missing required variables to a review queue. Step 5 applies the emergency escalation rule and creates separate urgent and routine queues from the score-valid pairs. Step 6 computes Priority(a) within each queue. Step 7 produces ranked lists with explanation sheets and a separate exclusion list. Step 8 logs all inputs, model versions, assumptions, thresholds, reference scales, and uncertainty statements for audit.

The explanation sheet is a required output. For each ranked action it should report the dominant terms in the priority equation, the source of evidence, any triggered risk prototypes or deterioration indicators, the cause of uncertainty, and a short plain-language rationale. In buildings this rationale may reference narrow doorways, level changes, inadequate illumination, or cluttered circulation paths represented in the digital twin. In bridges it may reference posterior deterioration probability, crack-growth trend, and observed covariates. In pavements it may reference the assumed maintenance schedule and annualized burden profile. [1] [2] [3]

Estimator selection for Ri_after_m or raw burden is optional and is outside the core EMPM score definition unless multiple candidate estimators are actually available for the same variable. In that case EMPM uses one reproducible default rule. Candidate estimators are trained on the same prespecified training folds and must output both point estimates and an uncertainty measure. For each candidate, compute two quantities on validation folds: mean absolute error for the target variable and empirical interval-coverage deviation, defined as the absolute difference between nominal and observed coverage for the declared prediction interval. If there are at least two candidates and both metrics show non-zero cross-candidate variance, standardize both quantities across candidates to z scores and form SelectionLoss = z(MAE) + z(CoverageDeviation). If there is only one candidate, select it by default and record that comparative selection was not possible. If the candidate set contains multiple models but either metric has zero cross-candidate variance, replace the undefined z score for that metric with 0 for all candidates and rank candidates by the remaining defined standardized metric; if both metrics have zero cross-candidate variance, choose the simpler model class in the order linear or Bayesian regression, decision tree, support vector method, generalized additive or boosting model, then neural network. If two candidates tie within a predeclared tolerance after these steps, apply the same simplicity order; if performance still ties, choose the model with shorter training time. This rule makes selection auditable without making any particular ensemble architecture mandatory. [4]

In portfolios with distributed sensing and multiple custodians, model updating may proceed through a federated architecture in which local sites retain raw data and share protected updates. Any such deployment should include differentiated privacy allocation, update clipping, and anomaly screening before aggregation. If privacy protection materially weakens anomaly detection, the resulting state estimates should be assigned higher uncertainty and subjected to stronger human review. [5]

7. Comparison with prior methods

Relative to the hierarchical Bayesian bridge model, EMPM adds an explicit action-ranking layer. The bridge model is strong in risk updating and uncertainty representation, but its direct output is deterioration assessment rather than a portfolio-wide ordering of maintenance actions. EMPM preserves Bayesian risk and uncertainty as inputs while adding consequence and burden terms needed for resource allocation across competing actions. [1]

Relative to the vision-based building framework, EMPM narrows the role of autonomous reasoning. The building framework performs iterative assessment over a structured scene representation. EMPM adopts the evidence architecture that binds measurements, semantics, and guideline-based risk prototypes, but the final prioritization remains rule-based and auditable. This is important because the building study also reports unresolved judgments and moderate precision in some cases; EMPM therefore represents such uncertainty explicitly rather than absorbing it into a single hazard label. [2]

Relative to the pavement life-cycle comparison study, EMPM shifts from comparing design or maintenance alternatives in isolation to ranking action-asset pairs under mixed portfolios. The pavement study supplies a rigorous basis for annualized burden metrics and for clear specification of functional unit and analysis boundary. EMPM embeds those burden metrics in a broader risk-based ranking rule, while accepting that direct transfer of pavement coefficients across regions is not justified. [3]

Relative to SGRE, EMPM borrows only two methodological ideas for upstream estimation tasks: uncertainty should be reported with predictions, and explanation should remain available at the component level. EMPM does not require forward or backward search ensembles, a stacking architecture, or SHAP outputs. Its fixed requirement is narrower: any estimator used for R_i_after_m or related quantities must be selected by a declared rule and must produce interpretable diagnostics together with uncertainty information. [4]

Relative to the federated learning security framework, EMPM has a different objective but a compatible infrastructure role. The federated study is concerned with privacy and poisoning robustness in distributed industrial data. EMPM does not propose a new security mechanism; it specifies when secure distributed learning becomes relevant and how limitations in privacy-robustness trade-offs should propagate into uncertainty reporting and governance. [5]

8. Prospective evaluation protocol

Because this article is conceptual, validation is prospective. The primary comparator is current practice, defined as expert-prioritized maintenance lists or single-source risk rankings that do not decompose burden and uncertainty. A secondary comparator is asset-class-specific prioritization without a common Layer 2 rule. An optional third comparator is a black-box predictive ranking system whose outputs are not decomposed into interpretable decision terms.

Stage 1 concerns technical validity of inputs. For bridges, inspection-only versus fused inspection-monitoring pipelines should be compared to record when posterior updating changes rank or urgency status. For buildings, digital-twin-derived hazard evidence should be compared with manual inspection annotations, including unresolved cases, to test whether traceability is preserved under imperfect closure. For pavements, burden calculations should be checked against the declared functional unit, analysis boundary, and maintenance schedule assumptions. [1] [2] [3]

Stage 2 concerns decision validity using an expert panel. The panel should contain 6 to 12 members. In a mixed portfolio, each represented asset class should contribute at least two domain experts, and at least one additional member should have portfolio-level responsibility for cross-class scheduling or capital planning. Every panelist evaluates every candidate action-asset pair across the full mixed portfolio, not only the actions from the panelist’s own technical specialty. To make that task executable, each candidate is presented in a standardized case sheet containing the same minimum information for all raters: asset class, asset identifier, proposed action, concise condition summary, consequence-criticality rubric entry, burden summary under the declared protocol, uncertainty summary, and any supporting images or traces allowed by the protocol. Before independent scoring, the panel receives a short calibration exercise on a separate training set to align use of the common rubric, but no discussion of the actual test cases is permitted. Each expert then submits an independent ordinal ranking of all candidate actions in the portfolio and an urgent/not-urgent label for each action. Consensus ranking is formed by median rank aggregation across all experts at the action level; if two or more actions share the same median rank, ties are resolved first by the smaller mean absolute deviation from that median, then by the lower mean assigned rank, and only then by a moderated adjudication meeting requiring at least two-thirds agreement. An action receives the expert urgent label if at least two-thirds of panelists mark it urgent. EMPM is then compared against this reference under two presentation modes: standard asset summaries and EMPM summaries with explanation sheets. Predefined outcomes are Spearman rank correlation with the consensus ordering, mean absolute rank deviation, proportion of actions for which the panel changes its preliminary judgment after viewing explanations, and proportion of EMPM explanations judged adequate by at least two-thirds of panelists. To avoid circularity, no portfolio used to finalize weights, urgency thresholds, burden-reference bounds, efficiency-reference bounds, or heuristic delta values may appear in the final Stage 2 test set.

Stage 3 concerns robustness and calibration. All tunable quantities—weight vectors, urgency thresholds, burden-reference bounds, efficiency-reference bounds, epsilon, and any heuristic delta values—must be fixed within a development stage that is separated from final assessment. The default protocol uses nested resampling when enough portfolios are available: an outer split reserves one or more portfolios for testing, while inner folds on the remaining portfolios are used for all tuning decisions. When the number of portfolios is too small for a stable outer holdout, leave-one-portfolio-out evaluation should be used, with every held-out portfolio scored only by parameters estimated from the remaining portfolios. Weight calibration is conducted only over feasible normalized weight vectors satisfying the declared admissibility rule, using the inner-fold expert consensus ranking as the reference target. Sensitivity analyses should vary urgency thresholds, uncertainty penalties, the decision horizon H, and R_i_after_m estimation pathways to assess rank stability. Urgency classification must follow one fixed operational rule throughout evaluation: an action is predicted urgent if and only if R_i(H) >= theta_R(class(i)) and C_i >= theta_C(class(i)); exact threshold equality is therefore assigned to the urgent queue. To examine instability near the thresholds, each portfolio should also report a threshold margin d_i = min{R_i(H) - theta_R(class(i)), C_i - theta_C(class(i))}. Actions with |d_i| <= delta, where delta is declared before analysis, form a boundary set for sensitivity review; boundary membership does not alter the primary urgent label, but rank changes inside this set are counted separately. Within each queue, ties in Priority(a) are resolved by the predeclared rule already used by EMPM. In distributed settings, simulated anomalous client updates may be introduced during federated training to examine whether security controls prevent unstable changes in R_i(H) or U_i. [5]

Urgent-action retrieval should be evaluated with a single primary target set and a single cutoff rule. For each portfolio, let U* denote the set of actions that receive the expert urgent label under Stage 2, and let U-hat denote the set of actions placed by EMPM in the urgent queue under the declared escalation rule. The primary metric is urgent recall, defined as |U-hat ∩ U*| / |U*| when |U*| > 0. If no action is labeled urgent by the panel, urgent recall is not computed for that portfolio; the protocol should instead report whether EMPM produced any false urgent assignments. To quantify over-escalation, the protocol should also report urgent precision, defined as |U-hat ∩ U*| / |U-hat| when |U-hat| > 0. No non-urgent action is included in either primary metric. If reviewers wish to inspect near-miss cases outside the urgent queue, that analysis should be reported separately as a sensitivity check using a fixed buffer q = max(1, ceil(0.10 x n_routine)), where n_routine is the number of routine-queue actions; the buffer consists of the q highest-ranked routine actions and is not merged with U-hat for the primary recall calculation. Additional outcomes include calibration of uncertainty categories, audit-trail completeness, and time required to produce a justified ranked list.

9. Failure cases, conflicting evidence, and limits of generalization

Several failure modes are foreseeable. If Ri is poorly estimated because data are sparse or biased, the transparency of the final ranking does not compensate for weak state estimation. The additive score may also understate interactions, for example when moderate deterioration becomes unacceptable only on highly critical assets. Preventive actions with long-term value may be undervalued when post-intervention risk reduction cannot be estimated beyond short horizons. In addition, EMPM scores are stable for audit only within portfolios or across portfolios scored under the same fixed reference scales and parameter protocol; if organizations alter those scales between programs, direct comparison of score magnitudes is not warranted.

The source evidence itself indicates mixed strengths. The bridge study focuses on crack evolution and bridge deck deterioration rather than every failure mechanism in bridge systems. The building study reports strong risk coverage in some settings but also moderate precision and incomplete judgment closure, indicating that explainable visual assessment can still leave unresolved cases. The pavement study offers a structured burden framework while explicitly warning that outcomes depend on regional and methodological assumptions. These observations justify caution about transfer across asset classes and jurisdictions. [1] [2] [3]

Distributed learning introduces another boundary condition. Collaborative modeling without raw data sharing may be desirable, but the security literature makes clear that privacy strength, anomaly detection, and training efficiency can conflict. If secure collaboration obscures malicious or unstable updates, the prioritization pipeline may inherit distorted asset-state estimates. In such settings, governance should allow fallback to local models, elevated uncertainty labels, or temporary suspension of automated ranking. [5]

Explainability can also mislead if it is reduced to decorative importance scores detached from evidence provenance. EMPM therefore treats explanation as a chain from evidence source to operational variable to ranking term. If any link in that chain is opaque or undocumented, the decision may appear more defensible than it is.

10. Conclusion

This article has established a bounded conceptual contribution: an explainable framework for maintenance prioritization across bridges, buildings, and pavements that integrates condition-risk, consequence criticality, intervention efficiency, life-cycle burden, and evidence uncertainty into an auditable action-ranking rule. The framework is grounded in direct prior evidence on bridge deterioration fusion, explainable building safety assessment, and explicit pavement life-cycle comparison, with methodological support from uncertainty-aware explainable modeling and secure distributed learning. [1] [2] [3] [4] [5]

The manuscript also resolves several operational issues that commonly weaken conceptual schemes. It separates raw burden from normalized burden, defines efficiency with a positive denominator applied only to valid burden records, constrains the joint calibration of efficiency and burden weights to reduce double-counting, and specifies concrete expert-consensus and urgent-label rules for prospective validation.

What remains untested is substantial. The proposed weights, urgency thresholds, uncertainty penalties, and cross-domain operationalizations have not yet been calibrated on a real multi-asset portfolio. The comparative value of EMPM against expert workflows, single-source rankings, or opaque machine-learning systems must therefore be established empirically. The present article offers an implementable decision architecture and a falsifiable evaluation protocol, not a completed demonstration of superior field performance.

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