Preliminary Study on Runway Pavement Friction Decay Using Data Mining

Luca, Mario De; Veropalumbo, Rosa; Pirozzi, Monica; Žilionienė, Daiva

doi:10.1016/j.trpro.2016.05.460

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…As it uses Chi-squared test for tree splitting strategy it is called Chi-squared Automatic Interaction Detection (CHAID). CHAID algorithm provides a splitting condition that is either dichotomist or multiple through a Chi-square test; each element of the tree generates two or more nodes (Luca et al, 2016). The methodology used in this study is based on method of classification and prediction of data mining as well as method of supervision with algorithms of CART and CHAID using IBM SPSS Modeler 14.2.…”

Section: Data Mining Methodsmentioning

confidence: 99%

The Application of the CART and CHIAD Algorithms in Sugar Beet Yield Prediction

Monjezi

2021

Basrah J. Agric. Sci.

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Yield prediction is a very important agricultural problem. Any farmer would like to know, as soon as possible, how much yield he can expect. The problem of predicting yield production can be solved by employing data mining techniques. This study evaluated the feasibility to predict the yield at Khuzestan Province in Iran using CART and CHAID algorithms. The analyses were performed using IBM SPSS Modeler 14.2. Three cropping seasons from 125 farms were selected between 2015 and 2018. The most important attributes were selected and the average yield was classified according to a decision tree. The data was partitioned into training (70%) and testing (30%) samples. The decision tree, including nine independent variables and 29 nodes, was produced through CART method. The decision tree, including nine independent variables and 39 nodes, was produced through the CHAID method. The CART and CHAID algorithms were evaluated using linear correlation and mean absolute error (MAE). Maximum precision of model in training part relevant to CART algorithm was equal to 95%, in testing part relevant to CART algorithm was equal to 93%. According to models′ precision, the results showed that CHAID and CART models were stable and suitable for prediction of sugar beet yield.

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Section: Data Mining Methodsmentioning

confidence: 99%

The Application of the CART and CHIAD Algorithms in Sugar Beet Yield Prediction

Monjezi

2021

Basrah J. Agric. Sci.

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“…The use of data mining techniques has been commended by previous studies due to its successes in diverse fields of research; Araujo et al, [43] used data mining for the estimation of energy consumption on the tire-pavement interaction for asphalt mixtures with different surface properties. Also, Luca et al [44] used the data mining approach on friction data to investigate runway pavement friction decay at the International Civil Airport of Lamezia Terme, Italy. These studies all stated the suitability and efficiency of data mining technique for describing system behaviour based on condition classification.…”

Section: The Concept Of Data Miningmentioning

confidence: 99%

Optimised Surface Condition Classification of Flexible Road Pavement Using AutoWEKA Model

Adeke

Zava

Tyogo

2020

Usak University Journal of Engineering Sciences

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The development of pavement management tools using intelligent algorithms requires a robust form of data mining -data classification for efficient and reliable results. The aim of this study is to investigate and optimally classify the surface condition of flexible road pavement along 60 km length of the Zaria -Kaduna Federal Highway in Northern Nigeria for maintenance decision. The study used data mining technique for the classification of pavement surface condition into good, satisfactory, fair, poor, very poor, serious or failed. A field survey was carried out to examine the surface area and length of various surface defects such as cracks, potholes, rutting and edge failure within chainages measuring 200 meters apart, which was used to compute the Pavement Condition Index (PCI) values and section classification in accordance with procedures stated in ASTM D6433. The AutoWEKA model of Waikato Environment for Knowledge Analysis (WEKA) software was used to optimally classify the surface condition of the highway. Results indicated that, 79.67% of the 300 total instances considered by the model were correctly classified while 20.33% of the instances were incorrectly classified. The optimum surface condition classification showed that worse pavement surface conditions of the sampled site were 'Poor', 'Very Poor' and 'Failed' at 77 (32.22%), 51 (21.34%) and 54 (22.59%) instances respectively of the correctly classified 239 instances out of the 300 total instances sampled. Based on its present condition, 76.15% of the road segment was bad. The rehabilitation or reconstruction of the Zaria -Kaduna Federal Highway was therefore recommended for improved condition and optimum performance.

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“…The authors have been conducting years of experimentation surveys at airports [33][34][35]. The surveys aimed to monitor air traffic, features of geometric infrastructure, the typological and physical/mechanical characteristics of pavement layers, and runway maintenance planning.…”

Section: Introductionmentioning

confidence: 99%

A Broad-Based Decision-Making Procedure for Runway Friction Decay Analysis in Maintenance Operations

et al. 2020

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The evaluation of friction is a key factor in monitoring and controlling runway surface characteristics. For this reason, specific airport management and maintenance are required to continuously monitor the performance characteristics needed to guarantee an adequate level of safety and functionality. In this regard, the authors conducted years of experimental surveys at airports including Lamezia Terme International Airport. The surveys aimed to monitor air traffic, features of geometric infrastructure, the typological and physical/mechanical characteristics of pavement layers, and runway maintenance planning. The main objective of this study was to calibrate specific models to examine the evolution of friction decay on runways in relation to traffic loads. The reliability of the models was demonstrated in the light of the significance of the friction measurement patterns by learning algorithms and considering the traffic data by varying the geometric and performance characteristics of the aircraft. The calibrated models can be implemented into pavement management systems to predict runway friction degradation, based on aircraft loads during the lifetime of the surface layers of the pavement. It is thus possible to schedule the maintenance activities necessary to ensure the safety of landing and takeoff maneuvers.

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Preliminary Study on Runway Pavement Friction Decay Using Data Mining

Cited by 7 publications

References 17 publications

The Application of the CART and CHIAD Algorithms in Sugar Beet Yield Prediction

The Application of the CART and CHIAD Algorithms in Sugar Beet Yield Prediction

Optimised Surface Condition Classification of Flexible Road Pavement Using AutoWEKA Model

A Broad-Based Decision-Making Procedure for Runway Friction Decay Analysis in Maintenance Operations

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