WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Article Citation - WoS: 11Citation - Scopus: 17Convolutional Neural Network-Based Deep Learning for Landslide Susceptibility Mapping in the Bakhtegan Watershed(Nature Portfolio, 2025) Feng, Li; Zhang, Maosheng; Mao, Yimin; Liu, Hao; Yang, Chuanbo; Dong, Ying; Nanehkaran, Yaser A.Landslides pose a significant threat to infrastructure, ecosystems, and human safety, necessitating accurate and efficient susceptibility assessment methods. Traditional models often struggle to capture the complex spatial dependencies and interactions between geological and environmental factors. To address this gap, this study employs a deep learning approach, utilizing a convolutional neural network (CNN) for high-precision landslide susceptibility mapping in the Bakhtegan watershed, southwestern Iran. A comprehensive landslide inventory was compiled using 235 documented landslide locations, validated through remote sensing and field surveys. An equal number of non-landslide locations were systematically selected to ensure balanced model training. Fifteen key conditioning factors-including topographical, geological, hydrological, and climatological variables-were incorporated into the model. While traditional statistical methods often fail to extract spatial hierarchies, the CNN model effectively processes multi-dimensional geospatial data, learning intricate patterns influencing slope instability. The CNN model outperformed other classification approaches, achieving an accuracy of 95.76% and a precision of 95.11%. Additionally, error metrics confirmed its reliability, with a mean absolute error (MAE) of 0.11864, mean squared error (MSE) of 0.18796, and root mean squared error (RMSE) of 0.18632. The results indicate that the northern and northeastern regions of the Bakhtegan watershed are highly susceptible to landslides, highlighting areas where proactive mitigation strategies are crucial. This study demonstrates that deep learning, particularly CNNs, offers a powerful and scalable solution for landslide susceptibility assessment. The findings provide valuable insights for urban planners, engineers, and policymakers to implement effective risk reduction strategies and enhance resilience in landslide-prone regions.Article Citation - WoS: 15Citation - Scopus: 15Deep Learning Method for Compressive Strength Prediction for Lightweight Concrete(Techno-press, 2023) Nanehkaran, Yaser A.; Azarafza, Mohammad; Pusatli, Tolga; Bonab, Masoud Hajialilue; Irani, Arash Esmatkhah; Kouhdarag, Mehdi; Derakhshani, RezaConcrete is the most widely used building material, with various types including high-and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.Article Citation - WoS: 12Citation - Scopus: 16A 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 - WoS: 41Citation - Scopus: 56Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs With Deep Learning Methods(Springer, 2022) Maras, Hadi Hakan; Ureten, KemalRheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis and osteoarthritis. In this retrospective study, the You Only Look Once (YOLO) algorithm was used to obtain hand images from original radiographs without data loss, and classification was made by applying transfer learning with a pre-trained VGG-16 network. The data augmentation method was applied during training. The results of the study were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated from the confusion matrix, and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. In the classification of rheumatoid arthritis and normal hand radiographs, 90.7%, 92.6%, 88.7%, 89.3%, and 0.97 accuracy, sensitivity, specificity, precision, and AUC results, respectively, and in the classification of osteoarthritis and normal hand radiographs, 90.8%, 91.4%, 90.2%, 91.4%, and 0.96 accuracy, sensitivity, specificity, precision, and AUC results were obtained, respectively. In the classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs, an 80.6% accuracy result was obtained. In this study, to develop an end-to-end computerized method, the YOLOv4 algorithm was used for object detection, and a pre-trained VGG-16 network was used for the classification of hand radiographs. This computer-aided diagnosis method can assist clinicians in interpreting hand radiographs, especially in rheumatoid arthritis and osteoarthritis.