Machine Learning to Predict Damage Mechanics in Trabecular Bone

机器学习预测骨小梁的损伤机制

• 批准号: RGPIN-2021-03280
• 负责人: Hardisty, Michael
• 金额: $ 2.33万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742473
• 关键词: Machine; Learning; Predict; Damage; Mechanics

Bone is a structurally complex biologic tissue, its mechanics are greatly affected by damage generated under physiologic and high impact loads. Characterizing bone fracture nucleation, propagation and stability of damaged bone tissue are challenging due to bone's complex ultrastructure, heterogeneous distribution of material, and the ubiquity of damage and flaws. Bone damage mechanics have been modelled deterministically with a variety of labour and computationally intensive formulations, but with only moderate agreement to experimental damage deposition. The effort to build such models has limited their optimization and application. The goal of this research program is to build hybrid machine learning (ML)-physics based models to better predict damage behaviour of structurally complex composite materials, with a focus on trabecular bone as a model system. Deep learning (DL) has many advantages for predicting damage and mechanical behaviour within tissues with complex ultrastructure: multi-scale effects can be integrated (through convolutional layers), generative deep models can represent the stochastic character of damage, the methods are tolerant of noise (typical of biological and damaging structures), and DL can incorporate non-linear relationships of material properties (often present in biological tissues). Once trained DL models can provide a computationally efficient and simple method for studying physical systems. Objectives: 1.Develop hybrid DL-physics models for the study of trabecular bone damage mechanics. 2.Quantify the non-linear interactions of material properties and multi-scale structure that affect normal damage mechanics of trabecular bone tissue. Interactions of tissue properties (mineralization, collagen crosslinking, hydrogen and covalent bonding within the collagen backbone) will be specifically investigated by ex vivo mechanical testing with damage labelling within µCT imaging. Hybrid DL-physics based models will be constructed combining neural networks with simplified linear-elastic finite element analysis and beam theory to predict apparent material behaviour and damage deposition. The structure will include networks for preprocessing (boundary conditions, material distribution, and geometry), post processing (damage deposition, and post-yield behaviour) and error correction neural networks to adjust simplified physics-based modelling results. This research will improve understanding of bone quality, allowing better prediction of tissue damage and mechanical stability. The tools developed will create a platform to study other composite engineered and biological materials with complex ultrastructure and a training platform for HQP in physics-based modeling and machine learning with applications in industry and academia. The methods for creating hybrid DL-physics models are also applicable to other areas of science where model formulation is challenging, noise complicates predictions, and large data sets exist.

骨骼是一种结构复杂的生物组织，其力学性能受到生理和高冲击载荷下产生的损伤的影响很大。由于骨骼复杂的超微结构、材料的不均匀分布以及普遍存在的损伤和缺陷，表征受损骨组织的骨折成核、扩展和稳定性具有挑战性。骨损伤力学已经通过各种劳动和计算密集型公式进行了确定性建模，但与实验损伤沉积的一致性程度较低。构建此类模型的努力限制了它​​们的优化和应用。该研究项目的目标是建立基于混合机器学习 (ML) 物理的模型，以更好地预测结构复杂复合材料的损伤行为，重点关注骨小梁作为模型系统。深度学习 (DL) 在预测具有复杂超微结构的组织内的损伤和机械行为方面具有许多优势：可以集成多尺度效应（通过卷积层），生成深度模型可以表示损伤的随机特征，该方法可以容忍噪声（典型的生物结构和损伤结构），并且 DL 可以结合材料特性的非线性关系（通常存在于生物组织中）。经过训练的深度学习模型可以为研究物理系统提供一种计算高效且简单的方法。目标： 1.开发用于研究小梁骨损伤力学的混合深度学习物理模型。 2.量化影响骨小梁组织正常损伤力学的材料特性和多尺度结构的非线性相互作用。组织特性（矿化、胶原交联、胶原骨架内的氢和共价键）的相互作用将通过体外机械测试和 µCT 成像中的损伤标记进行专门研究。将构建基于混合深度学习物理的模型，将神经网络与简化的线弹性有限元分析和梁理论相结合，以预测表观材料行为和损伤沉积。该结构将包括用于预处理（边界条件、材料分布和几何形状）、后处理（损伤沉积和屈服后行为）和误差校正神经网络的网络，以调整简化的基于物理的建模结果。 这项研究将增进对骨质量的了解，从而更好地预测组织损伤和机械稳定性。开发的工具将创建一个平台来研究具有复杂超微结构的其他复合工程材料和生物材料，并为 HQP 在基于物理的建模和机器学习及其在工业界和学术界的应用中提供培训平台。创建混合深度学习物理模型的方法也适用于模型制定具有挑战性、噪声使预测复杂化以及存在大量数据集的其他科学领域。