Landslide Risk Assessment by Using a New Combination Model Based on a Fuzzy Inference System Method

Landslides are one of the most dangerous phenomena that pose widespread damage to property and human lives. Over the recent decades, a large number of models have been developed for landslide risk assessment to prevent the natural hazards. These models provide a systematic approach to assess the risk value of a typical landslide. However, often models only utilize the numerical data to formulate a problem of landslide risk assessment and neglect the valuable information provided by experts’ opinion. This leads to an inherent uncertainty in the process of modelling. On the other hand, fuzzy inference systems are among the most powerful techniques in handling the inherent uncertainty. This paper develops a powerful model based on fuzzy inference system that uses both numerical data and subjective information to formulate the landslide risk more reliable and accurate. The results show that the proposed model is capable of assessing the landslide risk index. Likewise, the performance of the proposed model is better in comparison with that of the conventional techniques

Machine Learning-Based Estimation of Soil’s True Air-Entry Value from GSD Curves

The application of machine learning (ML) methods has proven to be promising in dealing with a wide range of geotechnical engineering problems in recent years. ML methods have already been used for the prediction of soil water retention curves (SWRC) and estimation of air-entry values (AEV). However, the reported works in the literature are generally based on limited data and conventional, less accurate approaches for AEV estimation. In this paper, a large database, known as UNsaturated SOil hydraulic DAtabase (UNSODA), is studied and the conventional and true AEVs of 790 soil samples are estimated based on determination methods reported in the literature. A ML approach is then employed for the development of a predictive model for the estimation of true AEV from water content-based SWRCs of a wide range of soil types taking into account the impact of bulk density and grain size distribution parameters. The obtained results reveal an enhanced accuracy in AEV determination, featuring R2 values of 0.964, 0.901 and 0.851 for training, validation, and testing data, respectively, which confirm the high-level performance of the developed ML model. Based on the results of a sensitivity analysis, the particle sizes of 50 and 250 µm are found to have the highest impact on the AEV estimation

Effective mechanical properties of multilayer nano-heterostructures

Two-dimensional and quasi-two-dimensional materials are important nanostructures because of their exciting electronic, optical, thermal, chemical and mechanical properties. However, a single-layer nanomaterial may not possess a particular property adequately, or multiple desired properties simultaneously. Recently a new trend has emerged to develop nano-heterostructures by assembling multiple monolayers of different nanostructures to achieve various tunable desired properties simultaneously. For example, transition metal dichalcogenides such as MoS2 show promising electronic and piezoelectric properties, but their low mechanical strength is a constraint for practical applications. This barrier can be mitigated by considering graphene-MoS2 heterostructure, as graphene possesses strong mechanical properties. We have developed efficient closed-form expressions for the equivalent elastic properties of such multi-layer hexagonal nano-hetrostructures. Based on these physics-based analytical formulae, mechanical properties are investigated for different heterostructures such as graphene-MoS2, graphene-hBN, graphene-stanene and stanene-MoS2. The proposed formulae will enable efficient characterization of mechanical properties in developing a wide range of application-specific nano-heterostructures

Effective Mechanical Properties of Multilayer Nano-Heterostructures

Two-dimensional and quasi-two-dimensional materials are important nanostructures because of their exciting electronic, optical, thermal, chemical and mechanical properties. However, a single-layer nanomaterial may not possess a particular property adequately, or multiple desired properties simultaneously. Recently a new trend has emerged to develop nano-heterostructures by assembling multiple monolayers of different nanostructures to achieve various tunable desired properties simultaneously. For example, transition metal dichalcogenides such as MoS2 show promising electronic and piezoelectric properties, but their low mechanical strength is a constraint for practical applications. This barrier can be mitigated by considering graphene-MoS2 heterostructure, as graphene possesses strong mechanical properties. We have developed efficient closed-form expressions for the equivalent elastic properties of such multi-layer hexagonal nano-hetrostructures. Based on these physics-based analytical formulae, mechanical properties are investigated for different heterostructures such as graphene-MoS2, graphene-hBN, graphene-stanene and stanene-MoS2. The proposed formulae will enable efficient characterization of mechanical properties in developing a wide range of application-specific nano-heterostructures

Rapid Bayesian optimisation for synthesis of short polymer fiber materials

The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative method which uses machine learning to optimise process development, incorporating multiple qualitative and quantitative objectives. We demonstrate the method with a novel fluid processing platform for synthesis of short polymer fibers, and show how the synthesis process can be efficiently directed to achieve material and process objectives

A Modeling Strategy for Predicting the Response of Steel Plate-Concrete Composite Walls

Shear walls are among lateral load resisting systems which are used to provide adequate stiffness, strength, and nonlinear deformation capacity to withstand strong ground motion. Usually at the base of the wall, these structures tolerate inelastic deformations subjected to strong ground motions. Researchers have offered composite walls to solve these problems. Steel plate-concrete composite (SCC) walls have been regarded as an alternative to reinforced concrete walls in terms of seismic performance and constructability. In this study, a new semi-macro modified fixed strut angle finite element model is proposed to predict the nonlinear response of SCC walls using OpenSees. A new modified fixed strut angle model and a quadrilateral flat shell element are adapted to the analysis of SCC shear walls. The numerical model is validated using the results of a set of experimental data reported in the literature. Comprehensive comparisons between analytical-model-predictions and experimental data suggest that the numerical model can accurately simulate the steel plate-concrete composite wall responses

Mechanical behaviour of photopolymerized materials

The photopolymerization process used for the production of additively manufactured polymers employed in advanced applications, enables to obtain objects spanning a large dimensional scale thanks to the molecular size achievable by the solidification process. In fact, the photopolymerization is based on the physical-chemical network cross-linking mechanism taking place at the nanoscale. Since the starting raw material is a liquid resin that progressively becomes solid upon the irradiation by a suitable light source, the mechanical properties – and so the corresponding mechanical response of the final printed structural material – heavily depend on the degree and distribution of the polymerization induced in the material itself. In the present study, starting from the governing equations of the light-induced polymerization process, we determine the chain density formed within the solid domain. Then, the mechanical response of photopolymerized elements obtained with different photopolymerization parameters is investigated. Moreover, the microstructure optimization of polymeric elements in relation to the achievement of the desired mechanical response with the least energy spent in the polymer’s formation, is studied. Finally, some interesting considerations related to the modelling of the photopolymerization process are outlined

Phase-field modelling of fluid driven fracture propagation in poroelastic materials considering the impact of inertial flow within the fractures

This paper presents a computational framework for modelling of fluid pressurised fracture propagation in saturated porous media. The framework rests on the principle of the variational phase-field theory to predict the fracture propagation pathway. The paper sets out the variational formulations and associated weak forms of the partial differential equations describing the pressure-deformation interplays of the fracturing domain, which are solved in the context of the Updated Lagrangian Finite Element method. The proposed formulation reflects the impact of the temporal evolution of the porous media attributes such as porosity, compressibility, permeability, and mechanical stiffness, on the nonlinear hydro-mechanical behaviour of the porous media during the fracture propagation. The inertial effect of the nonlinear flow inside the fracture is resolved using Forchheimer equation. Robustness of the modelling framework is examined by simulating benchmark examples. The effects of poroelastic characteristics of porous media such as the compressibility of solid skeleton and drained bulk modulus on the hydro-mechanical and cracking behaviour of porous rocks and on the total energy of the system are addressed. The nonlinearity of the fluid flow is found to be influential on the length of the leak-off and flow-back regions across the fractured zones, and on the amount of the fluid to be exchanged between the fractures and the porous zone, which is important in the prediction of the productivity of the fracking process in engineering applications

A software framework for probabilistic sensitivity analysis for computationally expensive models

A software framework for probabilistic sensitivity analysis for computationally expensive model