Mechanical, structural and microstructural investigations of a novel concrete for special structural applications

Degradation of concrete members exposed to sulphuric acid environments is a key durability issue that affects the life cycle performance and maintenance costs of civil infrastructures. Groundwater, chemical waste, sulphur bearing compounds in backfill, acid rain in industrial zones and biogenic acid in sewage systems are the main sources of sulphuric acid affecting concrete structures. In this research, as part of an ongoing research on development of novel concretes for special applications, an acid resistant mortar (ARM) with current applications in lining and repair purposes was converted to acid resistant concrete in the laboratory and investigated for structural applications in acidic environments. Mechanical properties of the initial acid resistant mortar material, this novel acid resistant concrete (ARC) and a type of conventional concrete (CC), as the control, have been studied in the laboratory subjected to an accelerated test, 7% (by volume) sulphuric acid. The studied mechanical properties included compressive strength, modulus of elasticity (MOE), modulus of rupture (MOR) and indirect tensile strength tests. Apart from acid resistance experiments, other important properties for a structural concrete such as drying shrinkage and concrete performance subjected to high rate strain loads and elevated temperatures were also evaluated for ARC and CC. Structural performance of reinforced concrete (RC) flexural members made of this new concrete (ARC) and CC was assessed before and after different periods of continuous immersion in 7% sulphuric acid solution through static and cyclic loading under four-point bending tests to detect the effects of acid attack on structural performance of RC beams. Load- deflection behaviour, curvature- moment resistance at mid span, ultimate load capacity, ductility factor, stiffness degradation, dissipated energy and damping ratio were the main variables studied in these experiments. Application of ARC in beam-column joints, as another application for this concrete was also investigated due to possessing higher ductility than conventional concrete in mechanical properties tests aiming at reduction of transverse reinforcing bars in such members and the potential for seismic applications. Structural elements (i.e., beams and joints) were also modelled by using FE software ATENA to analyse the experimental results numerically. Microstructural characterisation was also performed on ARC and CC samples before and after acid exposure using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray mapping (XRM) and X-ray diffraction (XRD) to gain a better understanding regarding the change of microstructure of materials after exposure to acid. ARC showed superior performance than CC after exposure to acid in terms of loss of mechanical properties. Structural performance of ARC has been comparable to CC before exposure to acid and after a long period of exposure to acid it showed better performance than CC, particularly in terms of load bearing capacity. The application of ARC in beam-column joints allowed reducing transverse reinforcing bars in these joints (50% compared to CC). Microstructural characterisation also revealed significant facts regarding the deterioration mechanism in both types of concretes and their effect on mechanical properties

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Multiscale Simulation and Machine Learning-assisted Performance Prediction for Cementitious Composites

Concrete is the most widely used construction material in the world for building infrastructures, bridges, and tunnels. However, cement production contributes to 5-7% of the global CO2 emissions, and thus, there is a significant need for innovative sustainable alternatives without compromising on the mechanical properties. Most of the approaches to address these concerns so far rely on the trial and error-based experimental response. Hence, robust multiscale simulation-based design approaches are needed to be developed for fundamental understanding as well as the comprehensive design of these new sustainable innovative materials. However, performing the simulations with millions of degrees of freedom could be daunting and requires high computation time. Thus, building data-driven models by training through the experimental/simulated data can serve as an efficient alternative to stimulate the design and development of new materials compositions for desired performance requirements. To address the poor sustainability credential-related issue, this thesis first evaluates the performance of geopolymers which are synthesized through alkaline activation of aluminosilicates. Analogous to calcium silicate hydrates (C-S-H) gel in cement paste, sodium aluminosilicate hydrate (N-A-S-H) gel is the primary binding phase in geopolymers formed via alkaline activation of fly ash. In this thesis, a realistic molecular structure of N-A-S-H geopolymer gels, inspired by the traditional calcium silicate hydrates gel, is proposed using molecular dynamics (MD) simulation. In contrast to the existing N-A-S-H model—where water is uniformly distributed in the structure— a layered-but-disordered structure is presented where water molecules are incorporated in the aluminosilicate network\u27s interlayer space. The developed structures are further validated using experimental observations. To address the durability performance of geopolymers, the dynamics of confined water and its interplay with alkali cations in disordered N-A-S-H gel are evaluated using reactive force field molecular dynamics. This is achieved by exploiting the evolution of mean squared displacements and the Van Hove correlation function. It is observed that the Si/Al ratio significantly influences the diffusion of confined water and sodium. Increased conversion of the Si–O–Na network to Si–O–H and Na–OH components with an increase in water content helps explain the alkali-leaching issue in fly ash-based geopolymers observed macroscopically. Moreover, the fracture properties of the disorder N-A-S-H gel are explored via molecular dynamics simulations. The simulated fracture toughness values of N-A-S-H are validated with the experimental results obtained using the nanoindentation technique, where the principle of conservation of energy is implemented to evaluate the fracture toughness from the load-penetration depth responses. Afterward, to address the issue related to the high computational demands of multiscale simulations, this thesis synergistically integrates multiscale simulations, experiments, and machine learning to predict the performance of various multiphysics responses of a wide variety of cementitious materials. Firstly, an ML model is developed using high throughput MD simulation that mapped the elastic properties with its chemical composition in C-S-H. The simulation results reveal that the influence of the silicate network on all the elastic constants of C–S–H is significantly higher than that of water and CaO content. Secondly, the strain-sensing ability of a nanoengineered self-sensing cementitious composite is predicted by synergistically integrating a validated FE analysis-based multiscale simulation framework with ML. The developed model predicts the strain-sensing response efficiently. Next, an ML-based model was developed for 3D printed cementitious auxetic cellular composites. With the advent of 3D printing, auxetic cellular cementitious composites (ACCCs) have recently garnered significant attention owing to their unique mechanical performance. Here, the prediction of Poisson\u27s ratio using ML approaches is developed by synergistically integrating an FE analysis-based framework with ML. Using SHAP, it is established that the volume fraction of voids is the most influential parameter in inducing auxetic behavior. In the end, an efficient ML model was developed to evaluate the non-linear composition-strength relationship in traditional concrete. Here, the ML model is trained using the experimental data available from the literature. However, the adopted dataset suffers from incompleteness because of missing data corresponding to different input features. To address the incompleteness in the dataset, different data imputation approaches are implemented for enhanced dataset completeness. The imputed dataset was leveraged to develop a complete ML model for concrete strength predictions. Besides, SHAP was implemented to evaluate the relative sensitivity of various performance descriptors, which can help toward the development of various high-performance concretes with unexplored compositional domains. Overall, validated performance prediction tools and various fundamental insights presented in this thesis help forward viable strategies toward the design and development of durable, resilient, and yet sustainable construction materials for next-generation civil infrastructure

Dynamic Mechanical Behaviors and Failure Mechanism of Lignite under SHPB Compression Test

There is an obvious impact effect of on-site blasting on the slope coal mass of open-pit mines, so it is of great significance to study the dynamic mechanical response characteristics of coal rock for slope stability control. In this paper, first, the mineral composition and microstructure of lignite from open-pit mine are analyzed, and it is found that the content of non-organic minerals in lignite such as clay accounts for more than 24.40%; meanwhile, the rock sample has obvious horizontal bedding characteristics and mainly micro pores and transition pores inside; further, there are obvious banded areas with high water content in the rock, which has the same extending direction as the beddings. Based on the SHPB test system, the dynamic compression tests of lignite with different impact velocities are carried out. The results show that there is a significant hardening effect caused by the increase of strain rate on the dynamic mechanical parameters of rock samples, and the stress–strain curve has obvious “double peak” characteristics; meanwhile, the macroscopic crack of the rock appears at the first stress peak and disappears after further compression until the interlayer fracture occurs; further, the fracture fractal dimension of lignite increases linearly with the impact velocity, revealing that the fragmentation of rock samples increases gradually. In addition, with the increase of impact velocity, the input energy and dissipated energy of rock samples increase linearly, while the elastic property increases slowly and at a low level. The bedding characteristics of lignite and the wave impedance difference between the layers cause the high-reflection phenomenon in the process of stress-wave propagation, and then produce the obvious tensile stress wave in the rock sample, which finally results in the interlayer fracture failure of the rock