Runway Instrumentation and Response Measurements

Al-Qadi, Imad L.; Portas, Silvia; Coni, Mauro; Lahouar, Samer

doi:10.3141/2153-18

Cited by 31 publications

(12 citation statements)

References 8 publications

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“…The runway and aprons are connected by a structured network of taxiways. In 2008, RWY 14/32 was completely reconstructed, and 150 sensors were embedded inside the new flexible pavement layers to acquire deformations, displacements, and pressures under real traffic and weather conditions [21][22][23][24].…”

Section: Case Studymentioning

confidence: 99%

Monitor Activity for the Implementation of a Pavement—Management System at Cagliari Airport

et al. 2021

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The conditions of airport movement-area pavements play a primary role on safety and regularity of airport operations; for this reason, the aerodrome operator needs to periodically survey their condition and provide their maintenance and rehabilitation in order to ensure the required operational characteristics. To meet these needs efficiently and effectively, the Airport Pavement-Management System (APMS) has proved to be a strategic tool to support decisions, aimed at defining a technically and economically sustainable management plan. This paper aims to investigate the theoretical elements and structure of the APMS; the appropriate methodologies to guarantee a constant updating of the system in all its aspects are presented, focusing on the specific case study of a medium-dimension Italian airport. The article describes the methods and the equipment used for the high-performance surveys and the condition indexes used for collecting and analyzing the data implemented to populate the APMS of Cagliari airport. Two major survey campaigns were carried out: the first in 2016 and the second in 2019. Both surveys were carried out using the same subdivision into sample units, following the ASTM D5340-12 criteria, to correctly compare data collected in different years. In order to sufficiently populate the APMS database, the measured and back-calculated data were stored and integrated using daily acquired pavement reports since 2009 and stored with the specific intention to develop customized decay curves for Cagliari Airport pavements. Preliminary results on the sustainable use of the APMS were reported even with data collected in a limited period and successfully applied to runway flexible pavement.

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Section: Case Studymentioning

confidence: 99%

Monitor Activity for the Implementation of a Pavement—Management System at Cagliari Airport

et al. 2021

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“…Pavement instrumentation is important for understanding pavement behavior under gear/axle loading. Depending on the approach used for simulating axle or gear loadings, the studies in the literature can be group into three categories: accelerated pavement testing ( 9 , 10 ), track test ( 11 , 12 ), and field instrumentation ( 13 , 14 ). In all these studies, the signals collected from various sensors were processed to extract responses generated by gear or axle loading.…”

Section: Data Pre-processingmentioning

confidence: 99%

Developing Machine-Learning Models to Predict Airfield Pavement Responses

Gungor

Al‐Qadi

2018

Transportation Research Record

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Aviation promotes trade and tourism by connecting regions, people, and countries. Having a functional and efficient airport pavement network is important to improve aviation traffic and to provide safer mobility to almost 800 million passengers travelling in the U.S. per year. The Federal Aviation Administration has initiated and actively been participating in many projects to further advance pavement design and performance to meet user requirements. To accomplish that, quantitative data are needed; such data may be collected from the pavement response to gear and environment loading. In this study, responses from four instrumented taxiway concrete slabs at John F. Kennedy International Airport were analyzed. The collected data were used to develop machine-learning (ML) based prediction models to compute the temperature, curling and bending strains within pavement. The ML models were developed using the support vector machine (SVM) algorithm. The results showed that SVM based ML models can predict pavement responses with a high accuracy and low computation time. Furthermore, in the case of feeding more data from various airports, ML models have proven to be a promising technique for pavement analysis engine for future airport pavement design frameworks. This study also produces recommendations for future data collection projects to have well-designed databases for data-driven models development.

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“…Two TDRs were installed 0.5 m deep in the subgrade, and two were installed in the middle of the granular base layer. Details about the instrumentation of runway pavement is available elsewhere (11)(12)(13).…”

Section: Instrumented Runway Pavementmentioning

confidence: 99%

Three-Dimensional Finite Element Modeling of Instrumented Airport Runway Pavement Responses

Wang

Al-Qadi

Portas

et al. 2013

Transportation Research Record

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The objective of this paper is to analyze runway pavement responses under moving\ud aircraft tire loading using a developed three-dimensional (3-D) finite element (FE) model.\ud The instrumented runway at Cagliari-Elmas airport (Sardegna, Italy) was modeled in the\ud analysis, which consists of a 350-mm asphalt layer, a 400-mm granular base layer, and\ud subgrade. The FE model characterized the asphalt layer as a linear viscoelastic material,\ud and two interface bonding conditions between asphalt layers (full bonding and partial\ud debonding) were used in the analysis. The aircraft tire loading was simulated using a\ud nontraditional loading assumption that represents the non-uniform distribution of tire\ud contact stresses along contact length and width under five ribs of an aircraft tire. Analysis\ud results show that traditional loading assumption that assumes uniform contact stresses at\ud the tire-pavement interface underestimates the critical tensile and shear strains in the\ud asphalt layer. In particular, the relatively high contact stresses at tire edge ribs under\ud heavy aircraft loading cause significant shear stresses at the pavement near-surface. The\ud pavement responses under various loading conditions (aircraft type, wheel load, and\ud speed) were calculated, and the critical responses were identified. Good agreements were\ud achieved when the calculated pavement responses (vertical pressure and horizontal strain)\ud at various locations were compared to the measured responses from field instrumentation.\ud The model results show that partial debonding between asphalt layers causes much\ud greater tensile strains at the bottom of the whole asphalt layer

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Runway Instrumentation and Response Measurements

Cited by 31 publications

References 8 publications

Monitor Activity for the Implementation of a Pavement—Management System at Cagliari Airport

Monitor Activity for the Implementation of a Pavement—Management System at Cagliari Airport

Developing Machine-Learning Models to Predict Airfield Pavement Responses

Three-Dimensional Finite Element Modeling of Instrumented Airport Runway Pavement Responses

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