scholarly journals Optimization of International Roughness Index Model Parameters for Sustainable Runway

2021 ◽  
Vol 13 (4) ◽  
pp. 2184
Author(s):  
Yu Tian ◽  
Shifu Liu ◽  
Le Liu ◽  
Peng Xiang
Keyword(s):  
Frequency Response ◽  
Dynamic Responses ◽  
Model Parameters ◽  
Response Distribution ◽  
Iri Model ◽  
International Roughness Index ◽  
Pavement Roughness ◽  
Study Results ◽  
Roughness Index ◽  
Roughness Evaluation

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.

scholarly journals Airport pavement roughness evaluation based on cockpit and center of gravity vertical accelerations

TRANSPORTES ◽  
2020 ◽  
Vol 28 (1) ◽  
pp. 147-159
Author(s):  
Jorge Braulio Cossío Durán ◽  
José Leomar Fernandes Júnior
Keyword(s):  
Center Of Gravity ◽  
International Roughness Index ◽  
Airport Pavement ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Roughness Evaluation ◽  
Vertical Accelerations

Pavimentos irregulares são geralmente responsáveis por acelerações verticais (VA) que afetam as aeronaves, aumentam a distância de parada e dificultam a leitura dos instrumentos de navegação na cabine dos pilotos. O International Roughness Index (IRI) e o Boeing Bump Index (BBI) são utilizados atualmente para quantificar a irregularidade longitudinal dos pavimentos aeroportuários e identificar seções que demandam atividades de manutenção e reabilitação (M&R). Contudo, tais índices baseiam-se apenas nas respostas dinâmicas de um automóvel a 80 km/h às irregularidades longitudinais dos pavimentos rodoviários, bem como nas características físicas das irregularidades (comprimento e altura), respectivamente, ignorando o efeito das VA nas aeronaves. Ainda, limites críticos atuais, sugeridos por Sayers & Karamihas e ANAC para IRI (2,0 e 2,5 m/km, respectivamente) e pela FAA para BBI (1,0) podem subestimar a condição real do pavimento. Este artigo avalia o efeito da irregularidade longitudinal nas acelerações na cabine dos pilotos (VACP) e no centro de gravidade (VACG). O software ProFAA permitiu calcular os índices e simular as VA em 4 aeronaves representativas atravessando 20 pistas de pouso e decolagem em 10 velocidades de taxiamento variando de 37 a 370 km/h. Comparações estatísticas e análises de regressão foram realizadas. Principais resultados mostram que VACP é 50% maior do que VACG e que ultrapassa o limite critico de 0,40 g quando o IRI e BBI estão acima de 3,7 m/km e 0,20, respectivamente. Um estudo de caso é também apresentado para comparar esses limites e sugere que a tomada de decisão baseada em IRI e VA pode trazer diferenças significativas na quantidade de atividades de M&R.

Pavement Roughness Indices Related to Riding Comfort

2014 ◽  
Vol 505-506 ◽  
pp. 180-183 ◽  
Author(s):  
Zong Tao Zhang ◽  
Quan Man Zhao ◽  
Wan Qiao Yang
Keyword(s):  
Evaluation Method ◽  
Ride Comfort ◽  
Vehicle Model ◽  
International Roughness Index ◽  
Poor Predictor ◽  
Pavement Roughness ◽  
Vibration Model ◽  
Roughness Index ◽  
Roughness Evaluation ◽  
Pitch Movement

The most widely used pavement roughness index is the international roughness index (IRI), but it is a poor predictor of ride comfort. In addition, the rider has not yet been included in the vehicle model used to evaluate pavement roughness. In this paper, in order to evaluate the comfort of the rider directly and consider the effects on ride comfort of pitch movement, a five-degree-freedom vibration model was built when a rider was added to a pitch-plane vehicle model. The vertical weighted root-mean-square (RMS) acceleration of the rider was suggested to be pavement roughness indices, which were related to ride comfort, respectively. The new roughness indices were calculated and a new pavement roughness evaluation method was developed.

scholarly journals Improvement of Boeing Bump Method Considering Aircraft Vibration Superposition Effect

2021 ◽  
Vol 11 (5) ◽  
pp. 2147
Author(s):  
Shifu Liu ◽  
Yu Tian ◽  
Le Liu ◽  
Peng Xiang ◽  
Zhekai Zhang
Keyword(s):  
Dynamic Responses ◽  
International Roughness Index ◽  
Pavement Preservation ◽  
Long Wave ◽  
Airport Pavement ◽  
Pavement Evaluation ◽  
Roughness Index ◽  
Response Thresholds ◽  
Roughness Evaluation ◽  
Superposition Effect

Pavement evaluation is critical for the decision-making process of pavement preservation and rehabilitation. Roughness is a key airport pavement characteristic that has been linked to impacts such as safety and service life. The Boeing Bump is one of the few roughness evaluation methods that has been developed specifically for runways. Although it is superior to the widely used International Roughness Index (IRI), it does not take into account the superposition effect of continuous runway bumps. Based on the ADAMS/Aircraft virtual prototype platform, this paper establishes and verifies five typical models (B737, B747, B757, B777, and B787) and then analyzes the most unfavorable speed (in terms of aircraft vibration) of each model and the dynamic responses caused by multiple bumps. The original Boeing Bump is improved and optimized by determining dynamic response thresholds for the various aircraft types. The results show that the revised Boeing Bump is more realistic than the original version, especially with regard to medium and long wave bands.

scholarly journals Local calibration of MEPDG for Superpave Pavements in Ontario : Enhance rutting calibration and preliminary study on IRI model

2021 ◽  
Author(s):  
Maryam Amir
Keyword(s):  
Permanent Deformation ◽  
Pavement Design ◽  
Local Calibration ◽  
Iri Model ◽  
International Roughness Index ◽  
Local Conditions ◽  
Calibration Methods ◽  
Roughness Index ◽  
Design Guide ◽  
Deformation Models

The AASHTO Mechanistic-Empirical Pavement Design Guide requires local calibration to account for local conditions, materials, and engineering practices. Previous local calibration studies in Ontario focused mainly on permanent deformation models for pavement rutting. The objectives of this study are twofold. First, to provide an enhanced calibration for the rutting models by using more vigilantly cross-verified input data and updated observed rutting data. Second, to perform a trial calibration for the international roughness index (IRI) model by considering three different calibration methods. Cracking models calibration, being performed by another colleague, has not yet been finalized; therefore, the IRI model calibration cannot be finalized in this study. Based upon 63 Superpave sections, the local calibration coefficients were found to be βAC = 1.7692, βT = 1.0, βN = 0.6262, βGB = 0.0968 and βSG = 0.2787 , which reduced the standard deviation of residuals to a value of 1 mm. The IRI calibration study found that the initial IRI value plays an important role in the calibration. Keywords: International Roughness Index (IRI) model; local calibration; Mechanistic-Empirical Pavement Design Guide (MEPDG); rutting model; Superpave.

Control Research on Asphalt Pavement Roughness in Anchorage Zone

2014 ◽  
Vol 1030-1032 ◽  
pp. 754-757
Author(s):  
Zheng Wang ◽  
Wei Zhang ◽  
Jia Jia Cheng ◽  
Meng Chen ◽  
Wen Jing Liu
Keyword(s):  
Asphalt Pavement ◽  
International Roughness Index ◽  
Retaining Structure ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Data Collection Instrument ◽  
Control Research ◽  
Main Factors ◽  
The Stability ◽  
Stability Of Structure

The effect of pavement roughness on the roadbed and retaining structure underground was studied. Three different groups of International Roughness Index (IRI) were analyzed based on 6221 data collection instrument in this paper. Results show that different pavement roughness has different effect on retaining structure. Additionally, the vibration RMS increases with IRI when it is in the range of normal driving for the car, but amplitude of the IRI is larger. Finally, the main factors which influence the stability of structure and some corresponding improving measures are presented.

Research on Relationship between Riding Quality and Pavement Distress of Asphalt Pavement

2012 ◽  
Vol 178-181 ◽  
pp. 1306-1313 ◽  
Author(s):  
Bo Peng ◽  
Lu Hu ◽  
Yang Sheng Jiang ◽  
Liang Yun
Keyword(s):  
Performance Evaluation ◽  
Nonlinear Regression ◽  
Asphalt Pavement ◽  
Theoretical Research ◽  
Pavement Performance ◽  
International Roughness Index ◽  
Pavement Distress ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Linear And Nonlinear

For asphalt pavement performance evaluation, pavement roughness, which is subject to cracks, potholes, road repairs and so on, is a major factor to influence riding quality. Therefore, riding quality is partly correlated with pavement distress, and the relationship can be transformed to that between pavement roughness and distress rate. However, this relationship is not clear, and not reflected in existing evaluation models. Thus, correlation analysis and non-parametric test of independent samples were applied in this paper to find that, international roughness index and pavement distress rate are significantly different due to different grades of roads, then, linear and nonlinear regression were used to analyze the relationships between international roughness index and pavement distress rate for different road grades. Furthermore, original data were processed by logarithmic transformation, radical transformation, exponential transformation and so on, based on which, corresponding relationships were analyzed by linear and nonlinear regression. Finally, best models to describe relationships between international roughness index and pavement distress rate for different road grades were solved out, and corresponding 90% confidence intervals were computed. Research in this paper offers a reference for improving asphalt pavement performance evaluation system and models, which is conducive to further theoretical research and practice.

Relationship between International Roughness Index and Power Spectral Density of Asphalt Pavements in China

2013 ◽  
Vol 742 ◽  
pp. 104-108
Author(s):  
Shao Wen Liu ◽  
Xiao Zhang
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Response Function ◽  
Asphalt Pavements ◽  
International Roughness Index ◽  
Quarter Car Model ◽  
Pavement Roughness ◽  
Power Spectral ◽  
Roughness Index ◽  
Quarter Car

In this paper, pavement roughness is assumed as random stationary variable and used as the exciting force of theoretical analyses of the quarter car model of International Roughness Index (IRI). From the frequency response function of the quarter car, the response function of the displacement difference between sprung and unsprung mass is obtained based on random process theory. Then the relationship between IRI and power spectral density (PSD) is established from statement characteristic of the response function. Finally, the longitudinal road profiles of typical asphalt roads in China are used to validate the proposed model.

Smartphone-Based Pavement Roughness Estimation Using Deep Learning with Entity Embedding

2020 ◽  
pp. 2050007
Author(s):  
Armstrong Aboah ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Deep Learning ◽  
Traditional Method ◽  
Road Surface ◽  
Accelerometer Data ◽  
International Roughness Index ◽  
Surface Information ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Accuracy Of Prediction ◽  
Deep Learning Model

The commonly used index for measuring pavement roughness is the International Roughness index (IRI). Traditional method for collecting road surface information is expensive and as such researchers over the years have resorted to other cheaper ways of collecting data. This study focuses on developing a deep learning model to quickly and accurately determine the IRI values of road sections at a cheaper cost. The study proposed a model that uses accelerometer data and previous year’s IRI values to predict current year IRI values. The study concludes that addition of accelerometer readings to previous year’s IRIs increased the accuracy of prediction.

scholarly journals Impact of Pavement Roughness on Traffic Safety under Heterogeneous Traffic Conditions

2020 ◽  
Vol 17 (1) ◽  
pp. 13-19
Author(s):  
M.O. Popoola ◽  
O.A. Apampa ◽  
O. Adekitan
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Heterogeneous Traffic ◽  
Ride Quality ◽  
International Roughness Index ◽  
Traffic Crash ◽  
Traffic Conditions ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Transportation Agencies

H ighway safety is a major priority for public use and for transportation agencies. Pavement roughness indirectly influence drivers' concentration, vehicle operation, and road traffic accidents, and it directly affect ride quality. This study focuses on analyzing the influence of pavement roughness on traffic safety using traffic, pavement and accident data on dual and single carriageway operated under heterogeneous traffic conditions in South-west, Nigeria. Traffic crash data between 2012 and 2015 was obtained from the Federal Road Safety Commission (FRSC) and International Roughness Index (IRI) data from the Pavement Evaluation Unit of the Federal Ministry of Works, Kaduna. Crash road segments represented 63 percent of the total length of roads. IRI values for crash and non-crash segments was a close difference of 0.3,This indicates that roughness is not the only factors affecting occurrence of traffic crashes but a combination with other factors such as human error, geometric characteristics and vehicle conditions. Crash severity was categorized into Fatal, serious and minor injury crashes. In all cases, the total crash rate increases with increase in IRI value up to a critical IRI value of 4.4 and 6.15 for Sagamu-Ore road and Ilesha-Akure-Owo road respectively, wherein the crash rate dropped. The conclusion is key in improving safety concerns, if transportation agencies keep their road network below these critical pavement conditions, the crash rate would largely decrease. The study concluded that ride quality does not directly affect traffic crash rate. Keywords: Pavement conditions, traffic safety, International Roughness Index, crash rate, carriageway.

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