# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 4 Conclusions  
This study analyzed the composite pavement performance deterioration of airports investigated, as well as the benefits of typical preventive maintenance technologies according to _LTPP_ data. Furthermore, this study proposed an optimization IDP algorithm for long-term preventive maintenance decision-making. Key findings and conclusions are as follows:  
1. The _PCI_ deterioration tendencies of the middle runway, terminal runway, and taxiway in five airports with composite pavements were analyzed, and the corresponding _PCI_ predictive models were regressed according to the long-term data of the investigated airports. The 13 coefficients \(a_{n}\), \(b_{n}\), \(c_{n}\) (\(n\) = 1, 2, 3, 4), and \(d\) in the _PCI_ predictive models for different composite pavement regions are shown in Table 3.
2. According to the LTPP database, _PJ_, _DRR_, and _PCI_ deterioration rates of 33 composite pavement sections treated with crack sealing, 28 with crack filling, 21 with fog seal, 19 with thin HMA overlay, and 11 with hot-mix recycled AC were analyzed. Thus, the maintenance benefit of each maintenance technology was determined. The regression relationship between _PJ_ and _PCI_ immediately before maintenance is shown in Figure 5, using a logarithmic model. For sections treated with crack sealing and crack filling, the DRR is nearly 0, and the _PCI_ deterioration rate is rarely influenced. For sections treated with fog seal, the average _DRR_ is 0.2, and the reduced _PCI_ deterioration rate returns to that immediately before maintenance after 2 years. For sections treated with thin HMA overlay and hot-mix recycled AC, the average _DRR_ is 0.7 and 0.8, and the recovering time of _PCI_ deterioration rate is 3 and 4 years, respectively. 3. A decision-making optimization method for long-term preventive maintenance based on DP was proposed, and the DP model parameters include stage variables, state variables, decision variables, state transfer equations, objective functions, basic equations, and constraints. An optimization IDP algorithm was developed to reduce the calculation cost by optimizing the calculation processes of both non-inferior solutions and the optimal solution. A method was used for preprocessing non-inferior solutions, and a median approach algorithm was proposed to reduce the time needed to compute the optimal solution. The decision-making optimization method was applied in a five-year preventive maintenance plan for composite pavements in Sunan Shuofang Airport, China, which sufficiently meets the demand for pavement performance without exceeding the annual maintenance budget. Figure 8: The five-year maintenance plan for Sunan Shuofang Airport. (**a**) Average _PCI_ curves; (**b**) Maintenance cost. 4. In this study, which maintenance technologies to adopt and when to maintain the pavement were dependent on the condition and deterioration tendency of composite pavements. Furthermore, the treatment costs were dependent on the type of maintenance technologies and inflation. However, the treatment costs were assumed to be irrelevant to composite pavement conditions, although poorer conditions may require greater treatment costs for the same maintenance technology. Moreover, the penalty for the maintenance of aircraft because of poor runway conditions, which may exacerbate aircraft deterioration and increase airline company costs, was not considered. These assumptions resulted in yielding \"no maintenance\" as the optimal solution in the application. The aforementioned problems must be studied in future work, including the relationship between treatment costs and pavement condition, as well as the more comprehensive constraints in the maintenance decision-making optimization method. Conceptualization, J.L. ; methodology, Z.W. ; validation, S.L. ; formal analysis, Z.W. ; investigation, Z.W. ; resources, S.L. ; data curation, S.L. ; writing--original draft preparation, Z.W. and Y.T. ; writing--review and editing, Z.W. and Y.T. ; supervision, J.L. ; project administration, J.L. ; funding acquisition, J.L.