[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124638-en":3,"doc-seo-124638-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124638,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation","Airport Pavement Management Systems rely on accurate monitoring of pavement deterioration over time to optimize maintenance priorities and identify the most damaged areas. Conventional practice uses Heavy Weight Deflectometer (HWD) non-destructive testing to measure impact-induced deflections at limited grid points, then backcalculate layer stiffness moduli. The study proposes a machine learning methodology to predict stiffness moduli at unsampled locations using a feedforward shallow neural network, trained with Bayesian regularization and k-fold cross-validation. Data augmentation expands the dataset from existing field results, yielding strong test correlations and enabling performance evaluation across paved areas.","Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation  \nNicola Baldo 1 , Fabio Rondinella 1 , and Clara Celauro2(B)   \n1 Polytechnic Department of Engineering and Architecture (DPIA), University of Udine, Via  \ndel Cotonificio 114, 33100 Udine, Italy  \n[nicola.baldo@uniud.it](nicola.baldo@uniud.it) , fabio .rondinella@phd .units .it  \n2 Department of Engineering, University of Palermo, Viale delle Scienze, Ed. 8, 90128 Palermo,  \nItaly  \n[clara.celauro@unipa.it](clara.celauro@unipa.it)  \nAbstract. For the purpose of the Airport Pavement Management System (APMS), in order to optimize the maintenance strategies, it is fundamental monitoring the pavement conditions’ deterioration with time. In this way, the most damaged areas can be detected and intervention can be prioritized. The conventional approach consists in performing non-destructive tests by means of a Heavy Weight Deflectometer (HWD) . This equipment allows the measurement of the pavement deflections induced by a defined impact load. This is a quite expensive and time-consuming procedure, therefore, the points to be investigated are usually limited to the center points of a very large mesh grid. Starting from the measured deflections at the impact points, the layers’ stiffness moduli can be backcalculated. This paper outlines a methodology for predicting such stiffness moduli, even at unsampled locations, based on Machine Learning approach, specificallyon a feedforward backpropagation Shallow Neural Network (SNN) . Such goal is achieved by processing HWD investigation and backcalculation results along with other variables related to the location ofthe investigation points and the underlying stratigraphy. Bayesian regularization algorithm and k-fold cross-validation procedure were both implemented to train the neural model. To enhance the training, a data analysis technique commonly referred to as data augmentation was used in order to increase the dataset by generating additional data from the existing ones.  \nThe results obtained during the model testing phase are characterized by a very satisfactory correlation coefficient, thus suggesting that the proposed Machine Learning approach is highly reliable. Notably, the proposed methodology can be implemented to evaluate the performance of every paved area.  \nKeywords: Airport pavement · Stiffness modulus · Data augmentation ·  \nMachine learning · Non-destructive testing data  \n© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023  \nA. Gomes Correia et al. (Eds.): ISIC 2022, LNCE 306, pp. 62–75, 2023 .  \n[https://doi.org/10.1007/978-3-031-20241-4](https://doi.org/10.1007/978-3-031-20241-4_5)[_](https://doi.org/10.1007/978-3-031-20241-4_5)[5](https://doi.org/10.1007/978-3-031-20241-4_5)  \n1 Introduction  \nAirport infrastructure network of a country, whether already developed or developing, is always astrategic asset for economic and social development[1]. It is therefore necessary to guarantee, despite all potential factors of degradation such as ageing, increase intraffic, budget shortfalls and financial constraints as in the pandemic since 2020 [2], a constant performance level so that the service offered may always meet the requirement for safety, efficiency and functionality. In airport infrastructures, great attention is paid to the runway as it is one of the core structure. In order to evaluate the deterioration level of a runway and its maintenance needs with time, both destructive [3] and nondestructive investigation techniques are usually performed. However, a gradual shift to non-destructive [4] testing (NDT) methods has been observed over the years. The former, by requiring borings, cores and excavation pits on an existing runway, require its temporary closure to traffic and cause a huge financial effort as well as service interruption [5] . The latter, on the other hand, thanks to recent hardware and software improvements, provid","cbCaifcdDvh8A72Z","https://ap.wps.com/l/cbCaifcdDvh8A72Z","pdf",2931894,1,14,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why are airport pavement stiffness moduli important for maintenance planning?\",\"answer\":\"They support monitoring deterioration over time so maintenance strategies can be optimized and the most damaged areas prioritized.\"},{\"question\":\"How does the conventional HWD-based approach work?\",\"answer\":\"HWD records pavement deflections from an impact load at selected grid points, then stiffness moduli are backcalculated using layer thickness and multi-layer elastic theory with iterative adjustment.\"},{\"question\":\"What machine learning and training methods are used to predict moduli at unsampled locations?\",\"answer\":\"A feedforward shallow neural network is trained with Bayesian regularization and k-fold cross-validation, and data augmentation is applied to increase the effective training dataset.\"}]","Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation | 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are airport pavement stiffness moduli important for maintenance planning?","Question",{"text":75,"@type":76},"They support monitoring deterioration over time so maintenance strategies can be optimized and the most damaged areas prioritized.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the conventional HWD-based approach work?",{"text":80,"@type":76},"HWD records pavement deflections from an impact load at selected grid points, then stiffness moduli are backcalculated using layer thickness and multi-layer elastic theory with iterative adjustment.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning and training methods are used to predict moduli at unsampled locations?",{"text":84,"@type":76},"A feedforward shallow neural network is trained with Bayesian regularization and k-fold cross-validation, and data augmentation is applied to increase the effective training 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