Bearing Strength of Unpaved Airport Surfaces Optimization of Number of Measurements

Cicmanec, Ludek; Marek, Vilem

doi:10.1109/miltechs.2019.8870107

Cited by 3 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While investigating the aviation safety databases, it was figured out that a large proportion of aircraft accidents occur at airports and particularly nearby runways. The majority of them are associated with runway excursions, accidental aircraft run-off, and over- and under-shooting a runway [ 2 ]. Therefore, as to address this, Ranieri et al proposes a gradual bearing strength decrease (17, 16, and 15 CBR) in three areas along a runway, 30–50, 50–65, and 65–105 m distant from the runway centerline [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Cicmanec

2021

Sensors

Self Cite

View full text Add to dashboard Cite

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Cicmanec

2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the conditions for runway excursions are still in place. Even statistical surveys of severe aircraft incidents and accidents in the European Union, as well as across the globe display that the majority of accidents involve aircraft runoff or undershoots [1,2]. Since the runway excursion incidents cannot be fully eliminated, the outcomes can be mitigated through a precise and thorough design and quality check of adjacent unpaved areas to paved runways.…”

Section: Introductionmentioning

confidence: 99%

Runway Excursions: Bearing Strength Control of Unpaved Areas

Cicmanec¹,

Ulrich²

2021

AiMT

View full text Add to dashboard Cite

The paper deals with the Dynamic Cone Penetrometer application which is intended to work as a vital means to support the bearing strength evaluation over the vast unpaved airport areas. Having identified fundamental drawbacks, the authors proposed some refinements in the original methodology. These predominantly respect the needs of airport operation services. Specifically, the model based on fuzzy logic and two tables, exceptionally suitable for day-to-day applications, are proposed and tested upon the extensive data set acquired at four airports over the last four years.

show abstract

Runway excursions: Bearing strength measurement concerns

Cicmanec

Petrasek²

2019

2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)

View full text Add to dashboard Cite

Bearing Strength of Unpaved Airport Surfaces Optimization of Number of Measurements

Cited by 3 publications

References 3 publications

Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Runway Excursions: Bearing Strength Control of Unpaved Areas

Runway excursions: Bearing strength measurement concerns

Contact Info

Product

Resources

About