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  1. Article ; Online: Semi-supervised learning framework for oil and gas pipeline failure detection

    Mohammad H. Alobaidi / Mohamed A. Meguid / Tarek Zayed

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    2022  Volume 11

    Abstract: Abstract Quantifying failure events of oil and gas pipelines in real- or near-real-time facilitates a faster and more appropriate response plan. Developing a data-driven pipeline failure assessment model, however, faces a major challenge; failure history, ...

    Abstract Abstract Quantifying failure events of oil and gas pipelines in real- or near-real-time facilitates a faster and more appropriate response plan. Developing a data-driven pipeline failure assessment model, however, faces a major challenge; failure history, in the form of incident reports, suffers from limited and missing information, making it difficult to incorporate a persistent input configuration to a supervised machine learning model. The literature falls short on the development of appropriate solutions to utilize incomplete databases and incident reports in the pipeline failure problem. This work proposes a semi-supervised machine learning framework which mines existing oil and gas pipeline failure databases. The proposed cluster-impute-classify (CIC) approach maps a relevant subset of the failure databases through which missing information in the incident report is reconstructed. A classifier is then trained on the fly to learn the functional relationship between the descriptors from a diverse feature set. The proposed approach, presented within an ensemble learning architecture, is easily scalable to various pipeline failure databases. The results show up to 91% detection accuracy and stable generalization ability against increased rate of missing information.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Evaluating the Role of Geofoam Properties in Reducing Lateral Loads on Retaining Walls

    Muhammad Imran Khan / Mohamed A. Meguid

    Sustainability, Vol 13, Iss 4754, p

    A Numerical Study

    2021  Volume 4754

    Abstract: Expanded polystyrene (EPS) geofoam is a lightweight compressible material that has been widely used in various civil engineering projects. One interesting application of EPS in geotechnical engineering is to reduce the lateral earth pressure on rigid non- ...

    Abstract Expanded polystyrene (EPS) geofoam is a lightweight compressible material that has been widely used in various civil engineering projects. One interesting application of EPS in geotechnical engineering is to reduce the lateral earth pressure on rigid non-yielding retaining walls. The compressible nature of the EPS geofoam allows for the shear strength of the backfill soil to be mobilized, which leads to a reduction in lateral earth pressure acting on the wall. In this study, a finite element model is developed and used to investigate the role of geofoam inclusion between a rigid retaining wall and the backfill material on the earth pressure transferred to the wall structure. The developed model was first calibrated using experimental data. Then, a parametric study was conducted to investigate the effect of EPS geofoam density, relative thickness with respect to the wall height, and the frictional angle of backfill soil on the effectiveness of this technique in reducing lateral earth pressure. Results showed that low-density EPS geofoam inclusion provides the best performance, particularly when coupled with backfill of low friction angle. The proposed modeling approach has shown to be efficient in solving this class of problems and can be used to model similar soil-geofoam-structure interaction problems.
    Keywords rigid retaining walls ; isolation efficiency ; finite element modeling ; lateral earth pressure ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
    Subject code 690
    Language English
    Publishing date 2021-04-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Predicting seismic-induced liquefaction through ensemble learning frameworks

    Mohammad H. Alobaidi / Mohamed A. Meguid / Fateh Chebana

    Scientific Reports, Vol 9, Iss 1, Pp 1-

    2019  Volume 12

    Abstract: Abstract The regional nature of liquefaction records and limited information available for a certain set of explanatories motivate the development of complex prediction techniques. Indirect methods are commonly applied to incidentally derive a hyperplane ...

    Abstract Abstract The regional nature of liquefaction records and limited information available for a certain set of explanatories motivate the development of complex prediction techniques. Indirect methods are commonly applied to incidentally derive a hyperplane to this binary classification problem. Machine learning approaches offer evolutionary prediction models which can be used as direct prediction methods to liquefaction occurrence. Ensemble learning is a recent advancement in this field. According to a predefined ensemble architecture, a number of learners are trained and their inferences are integrated to produce stable and improved generalization ability. However, there is a need to consider several aspects of the ensemble learning frameworks when exploiting them for a particular application; a comprehensive evaluation of an ensemble learner’s generalization ability is required but usually overlooked. Also, the literature falls short on work utilizing ensemble learning in liquefaction prediction. To this extent, this work examines useful ensemble learning approaches for seismic-induced liquefaction prediction. A comprehensive analysis of fifteen ensemble models is performed. The results show improved prediction performance and diminishing uncertainty of ensembles, compared with single machine learning models.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2019-08-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Article: Robust ensemble learning framework for day-ahead forecasting of household based energy consumption

    Alobaidi, Mohammad H / Fateh Chebana / Mohamed A. Meguid

    Applied energy. 2018 Feb. 15, v. 212

    2018  

    Abstract: Smart energy management mandates a more decentralized energy infrastructure, entailing energy consumption information on a local level. Household-based energy consumption trends are becoming important to achieve reliable energy management for such local ... ...

    Abstract Smart energy management mandates a more decentralized energy infrastructure, entailing energy consumption information on a local level. Household-based energy consumption trends are becoming important to achieve reliable energy management for such local power systems. However, predicting energy consumption on a household level poses several challenges on technical and practical levels. The literature lacks studies addressing prediction of energy consumption on an individual household level. In order to provide a feasible solution, this paper presents a framework for predicting the average daily energy consumption of individual households. An ensemble method, utilizing information diversity, is proposed to predict the day-ahead average energy consumption. In order to further improve the generalization ability, a robust regression component is proposed in the ensemble integration. The use of such robust combiner has become possible due to the diversity parameters provided in the ensemble architecture. The proposed approach is applied to a case study in France. The results show significant improvement in the generalization ability as well as alleviation of several unstable-prediction problems, existing in other models. The results also provide insights on the ability of the suggested ensemble model to produce improved prediction performance with limited data, showing the validity of the ensemble learning identity in the proposed model. We demonstrate the conceptual benefit of ensemble learning, emphasizing on the requirement of diversity within datasets, given to sub-ensembles, rather than the common misconception of data availability requirement for improved prediction.
    Keywords case studies ; data collection ; energy ; households ; infrastructure ; models ; prediction ; France
    Language English
    Dates of publication 2018-0215
    Size p. 997-1012.
    Publishing place Elsevier Ltd
    Document type Article
    ZDB-ID 2000772-3
    ISSN 0306-2619
    ISSN 0306-2619
    DOI 10.1016/j.apenergy.2017.12.054
    Database NAL-Catalogue (AGRICOLA)

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