Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach

Morales, Francisco J.; Reyes, Antonio; Cáceres, Noelia; Romero, Luis; Benítez, Francisco G.; Morgado, João; Duarte, Emanuel Felipe; Martins, Teresa

doi:10.1088/1757-899x/236/1/012107

Cited by 4 publications

(2 citation statements)

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“…The learning process of a computer when it is not explicitly programmed is referred to as machine learning [41]. In recent years, the use of machine learning has attracted the attention of researchers in the prediction of maintenance activities [42]. A machine learning technique investigates deep inter/intra-correlations and patterns in a dataset with minimal human intervention.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

Karimzadeh

Shoghli

2020

Civ Eng J

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With the pressing need to improve the poorly rated transportation infrastructure, asset managers leverage predictive maintenance strategies to lower the life cycle costs while maximizing or maintaining the performance of highways. Hence, the limitations of prediction models can highly impact prioritizing maintenance tasks and allocating budget. This study aims to investigate the potential of different predictive models in reaching an effective and efficient maintenance plan. This paper reviews the literature on predictive analytics for a set of highway assets. It also highlights the gaps and limitations of the current methodologies, such as subjective assumptions and simplifications applied in deterministic and probabilistic approaches. This article additionally discusses how these shortcomings impact the application and accuracy of the methods, and how advanced predictive analytics can mitigate the challenges. In this review, we discuss how advancements in technologies coupled with ever-increasing computing power are creating opportunities for a paradigm shift in predictive analytics. We also propose new research directions including the application of advanced machine learning to develop extensible and scalable prediction models and leveraging emerging sensing technologies for collecting, storing and analyzing the data. Finally, we addressed future directions of predictive analysis associated with the data-rich era that will potentially help transportation agencies to become information-rich.In classifying highway asset items, it should be noted that state agencies have different classification. For example, California Department of Transportation (CalTrans) categorizes transportation assets in primary classes of pavements, bridges, culverts, and Intelligent Transportation Systems (ITS) [4]. However, North Carolina DOT classifies assets into seven primary groups of pavements, bridges, tunnels, roadside features, pavement markings, rest areas, and maintenance yards [10]. Among these categories, that are different from state to state, we reviewed prediction methods for a subset of assets, including pavements, pavement markings, traffic signs, barriers, and culverts.We focused mostly on recent papers published in the past 20 years. To the best of our knowledge, we included most of the significant research studies reporting the application of predictive analytics. The total number of the investigated papers versus years of publication is shown in Figure 2.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

Karimzadeh

Shoghli

2020

Civ Eng J

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“…cuantificación del estado de los activos, gestión de alertas) y un sistema de apoyo a la toma de decisiones que recibe los resultados de estas herramientas y optimiza las intervenciones de mantenimiento. Esta comunicación presenta avances en el marco metodológico y analítico, y se avala con resultados obtenidos en un caso piloto (Infralert, 2016(Infralert, , 2017Morales et al, 2017Morales et al, , 2018Morales et al, , 2020. Las alertas estimadas se evalúan, de acuerdo con la información que brinda el sistema de toma de decisiones, en base a la evolución del estado de los activos de interés, reflejado por sus variables explicativas (e.g.…”

Section: Introductionunclassified

Técnicas de aprendizaje automático para la predicción de intervenciones de mantenimiento en infraestructuras lineales de carretera

Sánchez¹

2021

Datasets

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RESUMENLas técnicas de mantenimiento predictivo persiguen disminuir la aparición de fallos imprevistos, y la ejecución de las intervenciones correctivas necesarias que deben llevarse a cabo con anterioridad a la aparición de los fallos.Esta comunicación presenta una metodología, que permite automatizar la predicción de alertas de mantenimiento en infraestructuras lineales de transporte, aplicada al caso de una red de carreteras. Combina información cuantitativa del estado del activo junto con datos de las intervenciones de mantenimiento operativos e históricos, haciendo uso de técnicas de analítica de datos y modelos de aprendizaje automático.

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A machine learning methodology to predict alerts and maintenance interventions in roads

Morales

Reyes

Cáceres

et al. 2020

Road Materials and Pavement Design

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Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach

Cited by 4 publications

References 0 publications

Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

Técnicas de aprendizaje automático para la predicción de intervenciones de mantenimiento en infraestructuras lineales de carretera

A machine learning methodology to predict alerts and maintenance interventions in roads

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