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The reliability of thimble tubes plays a critical role for maintaining the safety of a nuclear power plant. The defect depth needs to be quantified and predicted to support the operational decision-making. This paper presents a method to quantify the defects on thimble tube wall based on the analyzation of eddy current testing (ECT) data. Then, a method using artificial neural network (ANN) to predict the detect depth is studied. The tubes are divided into 2 shapes and four regions according to their positions and the data of each region and each shape is expanded by mean interpolation. A prediction model based on ANN is constructed for each shape in each region. The experimental results show that the model can predict the signal of the next year according to the signal of the previous three years with mean absolute percentage error less than 16%. [ABSTRACT FROM AUTHOR]

BACKGROUND: Milling operations of laminae in spinal surgery generate high temperatures, which can lead to thermal injury and osteonecrosis and affect the biomechanical effects of implants, ultimately leading to surgical failure. OBJECTIVE: In this paper, a backpropagation artificial neural network (Bp-ANN) temperature prediction model was developed based on full factorial experimental data of laminae milling to optimize the milling motion parameters and to improve the safety of robot-assisted spine surgery. METHODS: A full factorial experiment design were used to analyze the parameters affecting the milling temperature of laminae. The experimental matrixes were established by collecting the corresponding cutter temperature T c and bone surface temperature T b for the milling depth, feed speed and different bone densities. The Bp-ANN lamina milling temperature prediction model was constructed from experiment data. RESULTS: Increasing milling depth increases bone surface and cutter temperature. Increasing feed speed had little effect on cutter temperature, but decreased bone surface temperature. Increasing bone density of laminae increased cutter temperature. The Bp-ANN temperature prediction model had best training results in the 10th epoch, and there is no overfitting (training set R = 0.99661, validation set R = 0.85003, testing set R = 0.90421, all temperature data set R = 0.93807). The goodness of fit R of Bp-ANN was close to 1, indicating that the predicted temperature was in good agreement with the experiment measurements. CONCLUSION: This study can help spinal surgery-assisted robot to select appropriate motion parameters at different density bones to improve lamina milling safety. [ABSTRACT FROM AUTHOR]

• Developing optimized neural network-based models for trapdoor stability prediction. • New FELA solutions for 3D rectangular trapdoors in anisotropic clays. • Developing hybrid machine learning models based on ANNs with various optimization algorithms. • Examining several performance metrics, regression plots, and rank analysis. The problem of trapdoor stability is a crucial problem in geotechnical engineering. This study is the first to introduce novel neural network-based metaheuristic models for the stability prediction of 3D rectangular trapdoors in anisotropic and nonhomogeneous clays. However, no researcher has considered such trapdoor problems in the past. In this study, the dataset is obtained by using finite element limit analysis (FELA). The proposed hybrid machine learning models based on artificial neural networks (ANNs) and various types of optimization algorithms (OAs), namely, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competition algorithm (ICA), and shuffled complex evolution (SCE), are also proposed in this paper by undergoing rigorous optimization to ensure accuracy and efficiency in capturing the intricate dynamics of the stability investigations from the models. The performance of the proposed ANN-based models is assessed using several performance metrics, regression plots, and rank analysis. The proposed ANN-SCE model outperforms the other proposed models in predicting trapdoor stability, where the ANN-SCE model achieved the highest rank, with a score of 58, followed by the ANN-ALO (47), ANN-ICA (33), and ANN-ACO (22) models. The proposed neural network-based metaheuristic models deliver precise and effective forecasts of trapdoor stability to make informed decisions concerning road design and mitigation tactics, ultimately improving the robustness of infrastructure facing geotechnical challenges. [ABSTRACT FROM AUTHOR]

Viscoelastic characterization of asphalt binders is critical for selecting suitable binders for a given climatic region and for improving long-term performance prediction using mechanistic-empirical pavement analysis and design methods, such as the Mechanistic-Empirical-Pavement-Design-Guide (MEPDG). A-VTS information (viscosity data) was used in the past and existing databases have this type of information. Nowadays, Dynamic Shear Modulus (|G*|) and Phase Angle (δ) determination using the Dynamic Shear Rheometer (DSR) is common practice. This resulted in the existence of old and new databases in highway agencies. The goal is to find a way to combine these separate data sets and merge them into a unified national database based on |G*| and δ. The work discussed in this paper is critical to make this merger possible and to use this in future binder to mixture predictive model developments and in MEPDG. In this study, |G*| and δ prediction models were developed. A machine learning technique named Artificial Neural Network (ANN) was employed in this process. 8940 data points from 41 binders were utilized in the model development process. After robust training, validation, and testing process, a |G*| prediction model with seven neurons and a δ prediction model with three neurons were reported. The models were compared with previous Bari-Witczak and Onifade-Birgisson models. ANN-based models performed better in every statistical parameter. Two equations were also extracted from the ANN models, which can be used reliably to predict |G*| and δ from A-VTS data. These two models with closed-form equations can be incorporated into the MEPDG for |G*| and δ predictions. • New models for Binder dynamic shear modulus and phase angle using binder viscosity data with temperature and frequency. • Artificial Neural Network (ANN) is utilized in the model development process. • An equation is extracted from each ANN-based model. • The prediction performance of new models is better than existing models in every statistical parameter. [ABSTRACT FROM AUTHOR]