Near-boundary error reduction with an optimised weighted Dirichlet-Neumann boundary condition for stochastic PDE-based Gaussian random field generators

Random field generation through the solution of stochastic partial differential equations is a computationally inexpensive method of introducing spatial variability into numerical analyses. This is particularly important in systems where material heterogeneity has influence over the response to certain stimuli. Whilst it is a convenient method, spurious values arise in the near boundary of the domain due to the non-exact nature of the specific boundary condition applied. This change in the correlation structure can amplify or dampen the system response in the near-boundary region depending on the chosen boundary condition, and can lead to inconsistencies in the overall behaviour of the system. In this study, a weighted Dirichlet–Neumann boundary condition is proposed as a way of controlling the resulting structure in the near-boundary region. The condition relies on a weighting parameter which scales the application to have a more dominant Dirichlet or Neumann component, giving a closer approximation to the true correlation structure of the Matérn autocorrelation function on which the formulation is based on. Two weighting coefficients are proposed and optimal values of the weighting parameter are provided. Through parametric investigation, the weighted Dirichlet–Neumann approach is shown to yield more consistent correlation structures than the common boundary conditions applied in the current literature. We also propose a relationship between the weighting parameter and the desired length-scale parameter of the field such that the optimal value can be chosen for a given problem

A statistical finite element method integrating a plurigaussian random field generator for multi-scale modelling of solute transport in concrete

A new model for the multi-scale simulation of solute transport in concrete is presented. The model employs plurigaussian simulations to generate stochastic representations of concrete micro- and meso-structures. These are idealised as two-phase medium comprising mortar matrix and pores for the micro-structure, and mortar and large aggregate particles for the meso-structure. The generated micro- and meso-structures are employed in a finite element analysis for the simulation of steady-state diffusion of solutes. The results of the simulations are used to calculate effective diffusion coefficients of the two-phase micro- and meso-structures, and in turn, the effective diffusion coefficient at the macro-scale at which the concrete material is considered homogenous. Multiple micro- and meso-structures are generated to account for uncertainty at the macro-scale. In addition, the level of uncertainty in the calculated effective diffusion coefficients is quantified through a statistical analysis. The numerical predictions are validated against experimental observations concerning the diffusion of chloride through a concrete specimen, suggesting that the generated structures are representative of the pore-space and coarse aggregate seen at the micro- and meso-scales, respectively. The method also has a clear advantage over many other structural generation methods, such as packing algorithms, due to its low computational expense. The stochastic generation method has the ability to represent many complex phenomena in particulate materials, the characteristics of which may be controlled through the careful choice of intrinsic field parameters and lithotype rules

Microcapsule triggering mechanics in cementitious materials: a modelling and machine learning approach

Self-healing cementitious materials containing microcapsules filled with healing agents can autonomously seal cracks and restore structural integrity. However, optimising the microcapsule mechanical properties to survive concrete mixing whilst still rupturing at the cracked interface to release the healing agent remains challenging. This study develops an integrated numerical modelling and machine learning approach for tailoring acrylate-based microcapsules for triggering within cementitious matrices. Microfluidics is first utilised to produce microcapsules with systematically varied shell thickness, strength, and cement compatibility. The capsules are characterised and simulated using a continuum damage mechanics model that is able to simulate cracking. A parametric study investigates the key microcapsule and interfacial properties governing shell rupture versus matrix failure. The simulation results are used to train an artificial neural network to rapidly predict the triggering behaviour based on capsule properties. The machine learning model produces design curves relating the microcapsule strength, toughness, and interfacial bond to its propensity for fracture. By combining advanced simulations and data science, the framework connects tailored microcapsule properties to their intended performance in complex cementitious environments for more robust self-healing concrete systems

A review of the occurrence and causes for wildfires and their impacts on the geoenvironment

Wildfires have short- and long-term impacts on the geoenvironment, including the changes to biogeochemical and mechanical properties of soils, landfill stability, surface- and groundwater, air pollution, and vegetation. Climate change has increased the extent and severity of wildfires across the world. Simultaneously, anthropogenic activities—through the expansion of urban areas into wildlands, abandonment of rural practices, and accidental or intentional fire-inception activities—are also responsible for a majority of fires. This paper provides an overall review and critical appraisal of existing knowledge about processes induced by wildfires and their impact on the geoenvironment. Burning of vegetation leads to loss of root reinforcement and changes in soil hydromechanical properties. Also, depending on the fire temperature, soil can be rendered hydrophobic or hydrophilic and compromise soil nutrition levels, hinder revegetation, and, in turn, increase post-fire erosion and the debris flow susceptibility of hillslopes. In addition to direct hazards, wildfires pollute air and soil with smoke and fire suppression agents releasing toxic, persistent, and relatively mobile contaminants into the geoenvironment. Nevertheless, the mitigation of wildfires’ geoenvironmental impacts does not fit within the scope of this paper. In the end, and in no exhaustive way, some of the areas requiring future research are highlighted

Stochastic periodic microstructures for multiscale modelling of heterogeneous materials

Plurigaussian simulation is a method of discrete random field generation that can be used to generate many complex geometries depicting real world structures. Whilst it is commonly applied at larger scales to represent geological phenomena, the highly flexible approach is suitable for generating structures at all scales. Here, an extension of plurigaussian simulation to periodic plurigaussian simulation (P-PGS) is presented, such that the resulting fields are periodic in nature. By using periodic Gaussian random fields as components of the method, periodicity is enforced in the generated structures. To substantiate the use of P-PGS in capturing complex heterogeneities in a physically meaningful way, the pore-scale microstructure of cement paste was represented such that its effective properties can be calculated through a computational homogenisation approach. The finite element method is employed to model the diffusion of heat through the medium under dry and saturated pore conditions, where numerical homogenisation is conducted to calculate the effective thermal conductivity of the medium. Comparison of the calculated values with experimental observations indicated that the generated microstructures are suitable for pore-scale representation, given their close match. A maximal error of 1.38% was observed in relation to the numerically determined effective thermal conductivity of mortar paste with air filled pores, and 0.41% when considering water filled pores. As the assumption of a periodic domain is often an underlying feature of numerical homogenisation, this extension of plurigaussian simulation enables a path for its integration into such computational schemes

Representation of three-dimensional unsaturated flow in heterogeneous soil through tractable Gaussian random fields

The representation of the spatial variability of soil properties is required to model non-uniform hydrological flow processes such as fingering and preferential moisture migration, which are prominent in both unsaturated and hydrophobic soils. This paper presents a new method for simulating three-dimensional hydrological flow processes in soil masses in which random fields of soil properties are conveniently generated through the numerical solution of a set of partial differential equations that have the same structure as those used for computing moisture transport. The method uses the Whittle–Matérn autocorrelation function to generate the required correlated Gaussian random field. A new method for representing statistical boundary effects on the degree of moisture infiltration is also presented. The statistical model component is integrated into a finite-element code for moisture transport and applied to the simulation of moisture infiltration and flow within an in situ layer of sandy loam. The transport properties and associated statistical measures employed to generate the random field are those reported in the experimental study. The results from the numerical simulations show that the model represents fingered flow behaviour with good accuracy, with the numerical and experimental infiltration fronts having similar characteristics and infiltration rates

Microcapsule Triggering Mechanics in Cementitious Materials: A Modelling and Machine Learning Approach.

Peer reviewed: TruePublication status: PublishedSelf-healing cementitious materials containing microcapsules filled with healing agents can autonomously seal cracks and restore structural integrity. However, optimising the microcapsule mechanical properties to survive concrete mixing whilst still rupturing at the cracked interface to release the healing agent remains challenging. This study develops an integrated numerical modelling and machine learning approach for tailoring acrylate-based microcapsules for triggering within cementitious matrices. Microfluidics is first utilised to produce microcapsules with systematically varied shell thickness, strength, and cement compatibility. The capsules are characterised and simulated using a continuum damage mechanics model that is able to simulate cracking. A parametric study investigates the key microcapsule and interfacial properties governing shell rupture versus matrix failure. The simulation results are used to train an artificial neural network to rapidly predict the triggering behaviour based on capsule properties. The machine learning model produces design curves relating the microcapsule strength, toughness, and interfacial bond to its propensity for fracture. By combining advanced simulations and data science, the framework connects tailored microcapsule properties to their intended performance in complex cementitious environments for more robust self-healing concrete systems