This study was undertaken to develop a new index, the Main Landing Gear Cumulative Stroke (MLGCS) index, to evaluate airport runway roughness. Using ADAMS/Aircraft software, we first developed and validated a virtual prototype model of the Boeing B737-800 aircraft and then employed the model to predict the aircraft’s dynamic responses with regard to roughness. Using this prototype model and comparing it to the International Roughness Index (IRI) that is designed to evaluate roadway roughness, we established a landing gear cumulative stroke (LGCS) model to represent runway roughness. The results show that taxiing speed is an important factor using the LGCS model. According to the most unfavorable scenario, we determined the LGCS of the nose landing gear to be 100 km/h and that of the main landing gear to be 60 km/h. These results underscore that, based on correlation analysis, using the cumulative stroke of the main landing gear is more reasonable than using that of the nose landing gear to evaluate runway roughness. Furthermore, based on measured data for 37 runways, a comparison of commonly used roughness indices indicates that the MLGCS index proposed in this paper is superior to both the Boeing Bump Index and the IRI.

Pavement roughness is a critical airport pavement characteristic that has been linked to impacts such as safety and service life. A properly defined roughness evaluation method would reduce airport operational risk, prolong the life of aircraft landing gear, and optimize the decision-making process for pavement preservation, which together positively contribute to overall airport sustainability. In this study, we optimized the parameters of the International Roughness Index (IRI) model to resolve the current poor correlation between the IRI and aircraft vibration responses in order to adapt and extend the IRI’s use for airport runway roughness evaluation. We developed and validated a virtual prototype model based on ADAMS/Aircraft software for the Boeing 737–800 and then employed the model to predict the aircraft’s dynamic responses to runway pavement roughness. By developing a frequency response function for the standard 1/4 vehicle model, we obtained frequency response distribution curves for the IRI. Based on runway roughness data, we used fast Fourier transform to implement the frequency response distribution of the aircraft. We then utilized Particle Swarm Optimization to determine more appropriate IRI model parameters rather than modifying the model itself. Our case study results indicate that the correlation coefficient for the optimized IRI model and aircraft vibration response shows a qualitative leap from that of the original IRI model.