Write 4 google search queries to search online that form an objective opinion from the following task: "machine learning for elastic modulus"

Assume the current date is October 31, 2024 if required.


You are a seasoned research assistant tasked with generating search queries to find relevant information for the following task: "machine learning for elastic modulus".
Context: [{'href': 'https://www.mdpi.com/2075-5309/14/10/3184', 'body': 'Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models             In this study, 10 different machine learning models were evaluated for their capacity to predict the elastic modulus of UHPC. XGBoost demonstrated the most potential for accurately predicting the elastic modulus of UHPC, particularly when a comprehensive dataset is available for model training. Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models. Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models. "Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models" Buildings 14, no. Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models. Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models. "Elastic Modulus Prediction of Ultra-High-Performance Concrete with Different Machine Learning Models" Buildings 14, no.'}, {'href': 'https://www.researchgate.net/publication/363648109_Machine_learning_framework_for_determination_of_elastic_modulus_without_contact_model_fitting', 'body': 'This paper presents an ensemble machine learning (ML) model for prediction of modulus of elasticity (MOE) of concrete formulated using recycled concrete aggregate (RCA), in relation to features of'}, {'href': 'https://onlinelibrary.wiley.com/doi/10.1002/suco.202300525', 'body': 'Automated machine learning techniques for estimating of elastic modulus of recycled aggregate concrete. Chen Chien-Ta, Chen Chien-Ta ... {RAC}} $$ because the current equations for the modulus of elasticity of concrete may not apply to recycled aggregate concrete (RAC) concrete. On the other hand, the Dwarf mongoose optimizer (DMO) and Phasor'}, {'href': 'https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-024-01459-z', 'body': 'In this work, four mainstream machine learning (ML) techniques are used to evaluate the bulk porosity and static elastic modulus of weathered Yungang Grottoes sandstone. Datasets are gathered from the experiments, which includes 432 groups effective experimental data including 8 inputs features. bulk porosity and static elastic modulus were considered as outputs to determine the weathering'}, {'href': 'https://link.springer.com/article/10.1007/s12540-024-01666-0', 'body': 'Elastic Modulus Prediction from Indentation Using Machine Learning: Considering Tip Geometric Imperfection | Metals and Materials International Article Access this article W.C. Oliver, G.M. Pharr, J. T.Y. Tsui, W.C. Oliver, G.M. Pharr, J. Kim, J. Lee, J. Kim, H.-J. Lee, J. J. Lee, O.M. Kwon, K. Kwon, H.N. Han, Int. J. S.H. Kim, B.W. Lee, Y. J. Kim, O.M. Kwon, J. Lee, H.-J. Lee, J.-Y. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Access this article'}, {'href': 'https://www.sciencedirect.com/science/article/pii/S0263823124001794', 'body': 'In this study, experimental, finite element (FE) simulation, machine learning (ML), and theoretical techniques are employed to investigate the in-plane elastic modulus (E HHSH) of hybrid hierarchical square honeycombs (HHSHs). First, HHSHs with different configurations were fabricated using a 3D printer, and in-plane quasi-static compression tests were conducted on them.'}, {'href': 'https://www.sciencedirect.com/science/article/pii/S0020768322004292', 'body': 'Two different methods are available for selecting an appropriate contact model: (1) the traditional approach based on prior knowledge of tip shape (Briscoe et al., 1994, Cappella and Dietler, 1999, Chang et al., 2016), or (2) an emerging machine learning (ML) approach based on the FZ features (Nguyen and Liu, 2022b). Well-trained ML models are an effective means of determining the elastic modulus of materials based on AFM FZ measurements (Nguyen and Liu, 2022a, Zhang et al., 2019). Drawing on the method proposed by the current group in a previous study for selecting an appropriate contact model (Nguyen and Liu, 2022b), this study presents an SML framework based on a regression model for calculating the elastic modulus of materials without the need for both contact model fitting and knowledge of tip shape.'}]

Use this context to inform and refine your search queries. The context provides real-time web information that can help you generate more specific and relevant queries. Consider any current events, recent developments, or specific details mentioned in the context that could enhance the search queries.

You must respond with a list of strings in the following format: ["query 1", "query 2", "query 3", "query 4"].
The response should contain ONLY the list.