土木工程材料自愈合行为的若干力学问题与研究进展

Application of self-healing engineering materials:Mechanical problems and research progress

为适应未来基础设施和工业化建造需求,如何改变传统土木工程结构和材料组成,打造全新的具有智能能力的基础设施,已经成为新的研究热点.工程结构的抗损坏能力直接影响国家的社会成本和经济效益.为了减少维修养护费用、提升结构的服役寿命,一种可行的方案是建造能够进行损伤自我愈合的拟生命系统.近几年来,微胶囊、电沉积、感应加热、微生物自愈合等技术被应用于土木工程与道路工程中,有望提升工程结构的耐久性及稳定性,延长服役寿命.但是,为提升自愈合工程材料的使用性能、精准预估裂纹扩展轨迹、精确预测材料的使用寿命,需要进一步从机理上解释自愈合行为.本文首先总结了自愈合材料在土木工程中应用的发展历程及研究进展,随后从损伤力学和断裂力学角度出发,分析了在解释和预测自愈合行为时所面临的若干力学问题,并对现有考虑自愈合效应的本构模型及数值算法进行了梳理.为了进一步明确各内外因素对裂纹扩展-愈合的正负耦合效应,从力学角度提出了亟待解决的问题与挑战.

In order to meet the needs of future infrastructure and industrial construction,namely building new advanced infrastructures with intelligent capabilities,the optimization of the structure and material composition design in traditional civil engineering has aroused general interest in both industrial and academic fields.The damage resistance of engineering structure has a direct impact on the social and economic cost to a country.To considerably reduce the maintenance cost and improve the service life of engineering structures,a feasible scheme is to build a life-like system with the capability of selfhealing.In recent years,techniques like microcapsule self-healing,electrodeposition self-healing,induction heating selfhealing and microbial self-healing have been widely used in civil engineering and road engineering,which are expected to improve the durability and stability of engineering structures,thus prolonging their service life to the maximum extent.However,the improvement of the performance of self-healing engineering materials and the accurate prediction of the crack propagating trajectory and the service life of the materials require a better understanding of the self-healing mechanism.In this review,the development and the advancement of the application of self-healing materials in civil engineering are firstly summarized.The self-healing mechanisms of cement concrete and asphalt,the two most used materials in civil engineering,are concluded,respectively.However,due to the low efficiency of the materials’natural selfhealing capability,some enhancement technologies are proposed and applied to improve the self-healing performance of those engineering materials,which are summarized in the later introduction.In the second part,the mechanical analysis for the explanation and prediction of the self-healing behaviors is introduced in detail based on damage and fracture mechanics.Besides,there are still some mechanical problems that remain unsolved,such as decoupling of elastic effect and self-healing effect on macro performance recovery and establishing a fatigue model considering damage evolution,crack propagation and self-healing effect,which are also summarized and analyzed.In the third part,the existing constitutive models considering self-healing effects and the relevant numerical algorithms are reviewed.Based on the three basic hypotheses,namely the strain equivalent hypothesis,the strain energy equivalent hypothesis and the power equivalent hypothesis,the effective configuration considering damage-healing effect is described.Additionally,relatively detailed review is given on the numerical algorithms considering self-healing mechanism with the aid of molecular dynamics(MD)and finite element method(FEM).Finally,the unsolved problems and challenges are proposed for further researches,where the coupling effects of the internal and external factors on the damage-fracture-healing process will be studied from a mechanical point of view.Therefore,the deep understanding of the healing and damage behavior of materials needs sufficient multi-scale analysis involving nano-scale,micro-scale,meso-scale and macro-scale.To sum up,self-healing materials play an important role in improving the durability and reliability of engineering structures.Therefore,the improvement of the self-healing efficiency is the key point to realize the wide and high efficient application of self-healing materials in civil engineering,which needs not only an essential understanding of the mechanical mechanisms but also fast numerical algorithms to accurately evaluate and predict the self-healing behavior.