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.

骨是一种结构复杂的生物组织，在生理和高冲击载荷作用下，其力学性能受损伤的影响很大。由于骨的超微结构复杂、材料分布不均以及损伤和缺陷的普遍存在，对骨折的成核、扩展和损伤骨组织的稳定性进行表征是具有挑战性的。骨损伤机制已经用各种劳动和计算密集的公式进行了确定性的建模，但与实验损伤沉积只有适度的一致性。建立这样的模型的努力限制了它们的优化和应用。该研究计划的目标是建立基于混合机器学习(ML)的物理模型，以更好地预测结构复杂的复合材料的损伤行为，重点是将松质骨作为模型系统。深度学习在预测具有复杂超微结构的组织内的损伤和力学行为方面具有许多优点：多尺度效应可以集成(通过卷积层)，生成性深度模型可以表示损伤的随机特征，方法具有容忍噪声的能力(典型的生物和损伤结构)，并且深度学习可以结合材料特性的非线性关系(通常存在于生物组织中)。一旦经过训练，动态链式模型就可以为研究物理系统提供一种计算高效且简单的方法。目的：1.建立骨小梁损伤力学研究的混合DL-物理模型。2.量化影响松质骨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。组织属性的相互作用(矿化、胶原交联、胶原主干内的氢键和共价键)将通过微CT成像中的损伤标记进行体外力学测试来具体研究。将神经网络与简化的线弹性有限元分析和梁理论相结合，构建基于混合DL-物理的模型，以预测表观材料行为和损伤沉积。该结构将包括用于前处理(边界条件、材料分布和几何)、后处理(损伤沉积和屈服后行为)的网络，以及用于调整简化的基于物理的建模结果的误差校正神经网络。这项研究将提高对骨质量的了解，使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研究具有复杂超微结构的其他复合工程和生物材料的平台，以及HQP在基于物理的建模和机器学习方面的培训平台，并在工业和学术界应用。用于创建混合DL-物理模型的方法也适用于其他科学领域，在这些领域，模型制定具有挑战性，噪声使预测复杂化，并且存在大数据集。