Cracks in cement concrete composites, whether autogenous or loading-initiated, are almost inevitable and often difficult to detect and repair, posing a threat to safety and durability of concrete infrastructures, especially for those with strict sealing requirements. The sustainable development of infrastructures calls for the birth of self-healing concrete composites, which has the built-in ability to autonomously repair narrow cracks. This paper reviews the fabrication, characterization, mechanisms and performances of autogenous and autonomous healing concretes. Autogenous healing materials such as mineral admixtures, fibers, nanofillers and curing agents, as well as autonomous healing methods such as electrodeposition, shape memory alloys, capsules, vascular and microbial technologies, have been proven to be effective to partially or even fully repair small cracks. As a result, the mechanical properties and durability of concrete infrastructure can be restored to some extent. However, autonomous healing techniques have shown a better performance in healing cracks than most of autogenous healing methods that are limited to healing of cracks having a width narrower than 150 μm. Self-healing concrete with biomimetic features, such as self-healing concrete based on shape memory alloys, capsules, vascular networks or bacteria, is a frontier subject in the field of material science. Self-healing technology provides concrete infrastructures with the ability to adapt and respond to the environment, exhibiting a great potential to facilitate the creation of a wide variety of resilient materials and infrastructures.

Self-healing cement concrete composites for resilient infrastructures: A review

Proceedings of the World Congress on Engineering 2013, WCE 2013

Self-healing is a natural phenomenon whereby living organisms respond to damage. Recently, considerable research efforts have been invested in self-healing cementitious materials that are capable of restoring structural integrity and mechanical properties after being damaged. Inspired by nature, a variety of creative approaches are explored here based on the intrinsic or extrinsic healing mechanism. Research on new intrinsic self-healing cementitious materials with biomimetic features is on the forefront of material science, which provides a promising way to construct resilient and sustainable concrete infrastructures. Here, the current advances in the development of the intrinsic healing cementitious materials are described, and a new definition of intrinsic self-healing discussed. The methods to assess the efficiency of different healing mechanisms are briefly summarized. The critical insights are emphasized to guide the future research on the development of new self-healing cementitious materials.

Recent Advances in Intrinsic Self-Healing Cementitious Materials.

Tests and methods of evaluating the self-healing efficiency of concrete: A review

From straw in bricks to modern use of microfibers in cementitious composites for improved autogenous healing – A review

Robust evaluation of self-healing efficiency in cementitious materials – A review

This paper presents a hybrid preconditioning technique for Conjugate Gradient method and discusses its parallel implementation on Graphic Processing Unit (GPU) for solving large sparse linear systems arising from application of interior point methods to conic optimization problems in the context of nonlinear Finite Element Limit Analysis (FELA) for computational Geomechanics. For large 3D problems, the use of direct solvers in general becomes prohibitively expensive due to exponentially growing memory requirements and computational time. Besides, the so-called saddle-point systems resulting from use of optimization framework is not an exemption. On the other hand, although preconditioned iterative methods have moderate storage requirements and therefore can be applied to much larger problems than direct methods, they usually exhibit high number of iterations to reach convergence. In present paper, we show that this problem can be effectively tackled using the proposed hybrid preconditioner along with an elaborate implementation on GPU. Furthermore, numerical results verify the robustness and efficiency of the proposed technique.

Application of a GPU-accelerated hybrid preconditioned conjugate gradient approach for large 3D problems in computational geomechanics

Direct solvers fail to solve these linear systems in large sizes, such as large 2D and 3D problems, due to their high storage and computational cost. This motivates using iterative methods. However, iterative solvers are not efficient for difficult problems without preconditioning techniques. In this paper, the effect of various preconditioning techniques on the convergence behavior of the preconditioned Conjugate Gradient (PCG) is investigated through a detailed comparative study. Furthermore, numerical results of applying PCG to several sample systems are presented and discussed thoroughly in a parametric study. Our results suggest that while incomplete Cholesky preconditioners are by far the most efficient techniques for sequential computations, significant gains may result from use of sparse approximate inverse methods in parallel environment in this field.

A comparative study of preconditioning techniques for large sparse systems arising in finite element limit analysis

The efficiency of parallel preconditioned conjugate gradient PCG algorithm for solving large sparse linear systems arising from application of interior point methods to conic optimisation problems in the context of nonlinear finite element limit analysis FELA for computational geomechanics is studied. For large 3D problems, the use of direct solvers in general becomes prohibitively expensive owing to exponentially growing memory requirements and computational time. And the so-called saddle-point systems resulting from use of optimisation framework is not an exemption. On the other hand, although preconditioned iterative methods have moderate storage requirements and therefore can be applied to much larger problems than direct methods, they usually exhibit high number of iterations to reach convergence. In the present paper, we show that this problem can be effectively tackled using efficient variants of sparse approximate inverse preconditioners along with an elaborate parallel implementation on multicore CPUs and significant improvements can be achieved by parallel implementation on graphic processing unit GPU. Furthermore, the efficiency of our proposed implementation is verified by the presented numerical results.

Parallel preconditioned conjugate gradient method for large sparse and highly ill-conditioned systems arising in computational geomechanics

The efficiency of several preconditioned Conjugate Gradient (PCG) schemes for solving of large sparse linear systems arising from application of second order cone programming in computational plasticity problems is studied. Direct solvers fail to solve these linear systems in large sizes, such as three dimensional cases, due to their high storage and computational cost. This motivates using iterative methods. However, iterative solvers are not efficient without preconditioning techniques for difficult problems. In this paper, the effect of different incomplete factorization preconditioning techniques on the convergence behavior of the preconditioned Conjugate Gradient (PCG) method to solve these large sparse and usually ill-conditioned linear systems is investigated. Furthermore, numerical results of applying PCG to several sample systems are presented and discussed. Several suggestions are also made as potential research subjects in this field.

Omid Kardani

Papers

Robust evaluation of self-healing efficiency in cementitious materials – A review

Application of a GPU-accelerated hybrid preconditioned conjugate gradient approach for large 3D problems in computational geomechanics

A comparative study of preconditioning techniques for large sparse systems arising in finite element limit analysis

Parallel preconditioned conjugate gradient method for large sparse and highly ill-conditioned systems arising in computational geomechanics

Iterative solution of large sparse linear systems arising from application of interior point method in computational geomechanics