Artificial Neural Networks

Reed, Steve

doi:10.1002/9780470061626.shm055

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Exploration of the intricate relationship between various inputs and their impact on mechanical properties necessitates a multifaceted analytical approach. Our investigation employs four distinct models-ANOVA [13], Random Forest Regression, [14] Partial Dependence Plots [15] from Artificial Neural Networks (ANNs) [16], and correlation analysis [17]-each chosen for its unique ability to interpret complex data. This integrative strategy not only discusses the rationale behind selecting these models and their implementation but also highlights the specific python libraries utilized, thereby providing a comprehensive view of the dataset's intricacies.…”

Section: (B) Analysis Of Input Importance On Mechanical Properties Us...mentioning

confidence: 99%

The Advanced Assessment of Nanoindentation-Based Mechanical Properties of a Refractory MoTaNbWV High-Entropy Alloy: Metallurgical Considerations and Extensive Variable Correlation Analysis

Sokoli,

Kamnis,

Delibasis

et al. 2024

Applied Sciences

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In the present study, a thorough examination of nanoindentation-based mechanical properties of a refractory MoTaNbVW high-entropy alloy (RHEA) was conducted. Basic mechanical properties, such as the indentation modulus of elasticity, indentation hardness, and indentation-absorbed elastic energy, were assessed by means of different input testing variables, such as the loading speed and indentation depth. The obtained results were discussed in terms of the elasto-plastic behavior of the affected material by the indentation process and material volume. Detailed analysis of the RHEA alloy’s nanoindentation creep behavior was also assessed. The effect of testing parameters such as preset indentation depth, loading speed, and holding—at the creep stage—time were selected for their impact. The results were explained in terms of the availability of mobile dislocations to accommodate creep deformation. Crucial parameters, such as maximum shear stress developed during testing (τmax), critical volume for dislocation nucleation (Vcr), and creep deformation stress exponent n, were taken into consideration to explain the observed behavior. Additionally, in all cases of mechanical property examination and in order to identify those input testing parameters—in case—that have the most severe effect, an extensive statistical analysis was conducted using four different methods, namely ANOVA, correlation matrix analysis, Random Forest analysis, and Partial Dependence Plots. It was observed that in most of the cases, the statistical treatment of the obtained testing data was in agreement with the microstructural and metallurgical observations and postulates.

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Section: (B) Analysis Of Input Importance On Mechanical Properties Us...mentioning

confidence: 99%

The Advanced Assessment of Nanoindentation-Based Mechanical Properties of a Refractory MoTaNbWV High-Entropy Alloy: Metallurgical Considerations and Extensive Variable Correlation Analysis

Sokoli,

Kamnis,

Delibasis

et al. 2024

Applied Sciences

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“…Another capability of SOMs is clustering data input through the features extracted from the original data input. A SOM uses the training process to organize the two-dimensional maps consisting of the topological links between neurons connected by means of weights connections [34]. Example of the strain field changes as a function of the coupling between damage and load conditions variation: (a) strain field change due to damage occurrence (five cumulatively induced holes and one artificial crack) for same load condition, (b) strain field change due to variation in load conditions (13 different pitch angles) for the pristine structure [24].…”

Section: Obs Based On Self Organizing Maps (Soms) and Two-level Clust...mentioning

confidence: 99%

Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

Sierra-Pérez

Torres-Arredondo

2017

Smart Mater. Struct.

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Structural health monitoring consists of using sensors integrated within structures together with algorithms to perform load monitoring, damage detection, damage location, damage size and severity, and prognosis. One possibility is to use strain sensors to infer structural integrity by comparing patterns in the strain field between the pristine and damaged conditions. In previous works, the authors have demonstrated that it is possible to detect small defects based on strain field pattern recognition by using robust machine learning techniques. They have focused on methodologies based on principal component analysis (PCA) and on the development of several unfolding and standardization techniques, which allow dealing with multiple load conditions. However, before a real implementation of this approach in engineering structures, changes in the strain field due to conditions different from damage occurrence need to be isolated. Since load conditions may vary in most engineering structures and promote significant changes in the strain field, it is necessary to implement novel techniques for uncoupling such changes from those produced by damage occurrence. A damage detection methodology based on optimal baseline selection (OBS) by means of clustering techniques is presented. The methodology includes the use of hierarchical nonlinear PCA as a nonlinear modeling technique in conjunction with Q and nonlinear-T 2 damage indices. The methodology is experimentally validated using strain measurements obtained by 32 fiber Bragg grating sensors bonded to an aluminum beam under dynamic bending loads and simultaneously submitted to variations in its pitch angle. The results demonstrated the capability of the methodology for clustering data according to 13 different load conditions (pitch angles), performing the OBS and detecting six different damages induced in a cumulative way. The proposed methodology showed a true positive rate of 100% and a false positive rate of 1.28% for a 99% of confidence.

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Comprehensive Evaluation of Soil Substrate Improvement Based on the Minimum Data Set Method

2022

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Long-term transitional grazing on the northern slopes of the Tianshan Mountains in Xinjiang has led to severe vegetation degradation, loss of self-renewal capacity and regional ecological degradation in the region. This study was conducted to improve the soil quality and vegetation restoration efficiency in the foreland zone of the northern slope of the Tianshan Mountains (Xiangyataizi slope) using xanthic acid, bentonite, a green plant growth regulator (GGR) and high amounts of mulch as improvement materials, and eight sets of experiments were conducted. Fifteen physical and chemical indicators were selected as the total data set (TDS), and the minimum data set (MDS) was constructed using principal component analysis (PCA) combined with norm values to evaluate the soils in the study area by nonlinear (NL) and linear (L) evaluation methods. The results showed that the soil quality evaluation indexes of the MDS included effective phosphorus, organic matter, percentage of powder, total potassium and total salt for the Xiangyataizi slope of the Tianshan Mountains. The SQI was (p < 0.05). The Ⅵ treatment significantly improved soil quality; that is, plastic mulch applied to soil with 250 g of fulvic acid, 1000 g of bentonite and 15 g of GGR (mixed with 100 kg of water) was the best treatment. Additionally, since the nonlinear soil quality evaluation method (SQI-NL) had a smaller variation interval and coefficient of variation of the soil quality index compared with linear soil quality evaluation method (SQI-L), the coefficient of determination between the MDS and TDS was 0.873 and 0.811 under the SQI-NL and SQI-L evaluation methods, respectively. The nonlinear soil quality evaluation method had better applicability in this region, and the minimum data set was more accurate for soil quality evaluation.

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Artificial Neural Networks

Cited by 3 publications

References 17 publications

The Advanced Assessment of Nanoindentation-Based Mechanical Properties of a Refractory MoTaNbWV High-Entropy Alloy: Metallurgical Considerations and Extensive Variable Correlation Analysis

The Advanced Assessment of Nanoindentation-Based Mechanical Properties of a Refractory MoTaNbWV High-Entropy Alloy: Metallurgical Considerations and Extensive Variable Correlation Analysis

Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

Comprehensive Evaluation of Soil Substrate Improvement Based on the Minimum Data Set Method

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