Fabian Diewald

During their life span, concrete structures interact with many kinds of external mechanical loads. Most of these loads are considered in advance and result in reversible deformations. Nevertheless, some of the loads cause irreversible, sometimes unnoticed changes below the macroscopic scale depending on the type and dimension of the impact. As the functionality of concrete structures is often relevant to safety and society, their condition must be known and, therefore, assessed on a regular basis. Out of the spectrum of non-destructive monitoring methods, Coda Wave Interferometry using embedded ultrasonic sensors is one particularly sensitive technique to evaluate changes to heterogeneous media. However, there are various influences on Coda waves in concrete, and the interpretation of their superimposed effect is ambiguous. In this study, we quantify the relations of uniaxial compression and uniaxial tension on Coda waves propagating in normal concrete. We found that both the signal correlation of ultrasonic signals as well as their velocity variation directly reflect the stress change in concrete structures in a laboratory environment. For the linear elastic range up to 30% of the strength, we calculated a velocity variation of −0.97‰/MPa for compression and 0.33%/MPa for tension using linear regression. In addition, these parameters revealed even weak irreversible changes after removal of the load. Furthermore, we show the time-dependent effects of shrinkage and creep on Coda waves by providing the development of the signal parameters over time during half a year together with creep recovery. Our observations showed that time-dependent material changes must be taken into account for any comparison of ultrasonic signals that are far apart in time. The study’s results demonstrate how Coda Wave Interferometry is capable of monitoring stress changes and detecting even small-size microstructural changes. By indicating the stated relations and their separation from further impacts, e.g., temperature and moisture, we anticipate our study to contribute to the qualification of Coda Wave Interferometry for its application as an early-warning system for concrete structures.

Damage in concrete structures initiates as the growth of diffuse microcracks that is followed by damage localisation and eventually leads to structural failure. Weak changes such as diffuse microcracking processes are failure precursors. Identification and characterisation of these failure precursors at an early stage of concrete degradation and application of suitable precautionary measures will considerably reduce the costs of repair and maintenance. To this end, a reduced order multiscale model for simulating microcracking-induced damage in concrete at the mesoscale level is proposed. The model simulates the propagation of microcracks in concrete using a two-scale computational methodology. First, a realistic concrete specimen that explicitly resolves the coarse aggregates in a mortar matrix was generated at the mesoscale. Microcrack growth in the mortar matrix is modelled using a synthesis of continuum micromechanics and fracture mechanics. Model order reduction of the two-scale model is achieved using a clustering technique. Model predictions are calibrated and validated using uniaxial compression tests performed in the laboratory.

Concrete is a heterogeneous material with a disordered material morphology that strongly governs the behaviour of the material. In this contribution, we present a computational tool called the Concrete Mesostructure Generator (CMG) for the generation of ultra-realistic virtual concrete morphologies for mesoscale and multiscale computational modelling and the simulation of concrete. Given an aggregate size distribution, realistic generic concrete aggregates are generated by a sequential reduction of a cuboid to generate a polyhedron with multiple faces. Thereafter, concave depressions are introduced in the polyhedron using Gaussian surfaces. The generated aggregates are assembled into the mesostructure using a hierarchic random sequential adsorption algorithm. The virtual mesostructures are first calibrated using laboratory measurements of aggregate distributions. The model is validated by comparing the elastic properties obtained from laboratory testing of concrete specimens with the elastic properties obtained using computational homogenisation of virtual concrete mesostructures. Finally, a 3D-convolutional neural network is trained to directly generate elastic properties from voxel data.

Concrete is a heterogeneous material with a disordered material morphology that strongly governs the behavior of the material. In this contribution, we present a computational tool called the Concrete Mesostructure Generator (CMG) for the generation of ultra-realistic virtual concrete morphologies for mesoscale and multiscale computational modeling and simulation of concrete. Given an aggregate size distribution, realistic generic concrete aggregates are generated by a sequential reduction of a cuboid to generate a polyhedron with multiple faces. Thereafter, concave depressions are introduced in the polyhedron using gaussian surfaces. The generated aggregates are assembled into the mesostructure using a hierarchic random sequential adsorption algorithm. The virtual mesostructures are first calibrated using laboratory measurements of aggregate distributions. The model is validated by comparing the elastic properties obtained from laboratory testing of concrete specimens with the elastic properties obtained using computational homogenisation of virtual concrete mesostructures. Finally, a 3D-convolutional neural network is trained to directly generate elastic properties from voxel data.

Damage in concrete structures initiates as the growth of diffuse microcracks that is followed by damage localisation and eventually leads to structural failure. Weak changes such as diffuse microcracking processes are failure precursors. Identification and characterisation of these failure precursors at an early stage of concrete degradation and application of suitable precautionary measures will considerably reduce the costs of repair and maintenance. To this end, a reduced order multiscale model for simulating microcracking-induced damage in concrete at the mesoscale levelis proposed. The model simulates the propagation of microcracks in concrete using a two-scale computational methodology. First, a realistic concrete specimen that explicitly resolves the coarse aggregates in a mortar matrix was generated at the mesoscale. Microcrack growth in the mortar matrix is modelled using a synthesis of continuum micromechanics and fracture mechanics. Model order reduction of the two-scale model is achieved using clustering technique. Model predictions are calibrated and validated using uniaxial compression tests performed in the laboratory.