Machine Learning to Predict Damage Mechanics in Trabecular Bone

骨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损伤的影响。由于骨的超微结构复杂，材料分布不均，损伤和缺陷普遍存在，表征骨折的成核、传播和受损骨组织的稳定性具有挑战性。骨损伤力学已经用各种劳动和计算密集的公式确定地建模，但只有适度的同意实验损伤沉积。建立这种模型的努力限制了它们的优化和应用。本研究计划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的复合材料的损伤行为，重点关注小梁骨作为模型系统。深度学习（DL）在预测具有复杂超微结构的组织中的损伤和力学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（典型的生物和损伤结构），并且DL可以纳入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效率高且简单的方法。目的:1。建立混合dl -物理模型用于研究骨小梁损伤力学。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。组织特性的相互作用（矿化、胶原交联、胶原骨架内的氢和共价键）将通过在微CT成像中进行损伤标记的离体力学测试进行具体研究。基于dl物理的混合模型将结合神经网络、简化的线弹性有限元分析和梁理论来预测材料的表观行为和损伤沉积。该结构将包括用于预处理（边界条件、材料分布和几何）、后处理（损伤沉积和后屈服行为）和纠错神经网络的网络，以调整简化的基于物理的建模结果。这项研究将提高对骨质量的理解，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，用于研究具有复杂超微结构的其他复合工程和生物材料，并为HQP在基于物理的建模和机器学习方面的培训平台，并应用于工业和学术界。创建混合dl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型数据集的其他科学领域。

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.