İnşaat Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/395

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  • Article
    Citation - WoS: 12
    Citation - Scopus: 16
    A Metaheuristic-Guided Machine Learning Approach for Concrete Strength Prediction With High Mix Design Variability Using Ultrasonic Pulse Velocity Data
    (Elsevier, 2023) Selcuk, S.; Tang, P.
    Assessment of concrete strength in existing structures is a common engineering problem. Several attempts in the literature showed the potential of ML methods for predicting concrete strength using concrete properties and NDT values as inputs. However, almost all such ML efforts based on NDT data trained models to predict concrete strength for a specific concrete mix design. We trained a global ML-based model that can predict concrete strength for a wide range of concrete types. This study uses data with high variability for training a metaheuristic-guided ANN model that can cover most concrete mixes used in practice. We put together a dataset that has large variations of mix design components. Training an ANN model using this dataset introduced significant test errors as expected. We optimized hyperparameters, architecture of the ANN model and performed feature selection using genetic algorithm. The proposed model reduces test errors from 9.3 MPa to 4.8 MPa.
  • Article
    Citation - Scopus: 3
    Strength Prediction of Engineered Cementitious Composites With Artificial Neural Networks
    (MIM RESEARCH GROUP, 2021) Yesilmen, S.
    Engineered Cementitious composites (ECC) became widely popular in the last decade due to their superior mechanical and durability properties. Strength prediction of ECC remains an important subject since the variation of strength with age is more emphasized in these composites. In this study, mix design components and corresponding strengths of various ECC designs are obtained from the literature and ANN models were developed to predict compressive and flexural strength of ECCs. Error margins of both models were on the lower side of the reported error values in the available literature while using data with the highest variability and noise. As a result, both models claim considerable applicability in all ECC mixture types. © 2021 MIM Research Group. All rights reserved.