PROCJENA JEDNOOSNE TLAČNE ČVRSTOĆE POMOĆU MODELA BAZIRANIH NA REGRESIJSKIM STABLIMA

This paper presents the estimation of the uniaxial compressive strength for mudstone and wackestone carbonates. The need for the estimation has occurred due to inability to fulfill the high quality requirements of sample treatment during direct determination of this physical and mechanical property on certain types of rocks. For the needs of modelling intact rock materials, extracted from six locations in Croatia, were tested. The following properties were examined: density, effective porosity, point load strength index, Schmidt rebound hardness, P-wave velocity and uniaxial compressive strength which was the target value of the used statistical models. The statistical models based on multiple linear regression and regression trees were considered and compared using cross validation, which showed that the most efficient estimation of the uniaxial compressive strength is obtained using random forestsOvaj rad bavi se procjenom jednoosne tlačne čvrstoće za karbonate tipa madston-vekston. Potreba procjene javlja se zbog nemogućnosti ispunjavanja propisane visoke kvalitete obrade uzoraka kod direktnog određivanja tog fizikalno-mehaničkog svojstva na nekim vrstama stijena. Za potrebe modeliranja, u ovom radu, ispitivan je intaktni stijenski materijal sa šest mjesta u Hrvatskoj. Ispitane značajke su: gustoća, efektivna poroznost, indeks čvrstoće, Schmidtova tvrdoća, brzina prolaza ultrazvučnog P-vala te jednoosna tlačna čvrstoća koja je bila i ciljana vrijednost procjene uspostavljenih modela. Prikazani modeli su načinjeni na temelju višestruke regresije i regresijskog stabla, a provedena unakrsna validacija, pokazala je kako najuspješniju procjenu jednoosne tlačne čvrstoće daje model slučajnih šuma (engl. random forests)

Characterizing the Nonlinear Behavior of Flakeboards

To predict accurately the failure load of a layered flakeboard in bending, the stress-strain relations appropriate for each layer must be known. This paper describes the application of a method for characterizing the nonlinear behavior of a flakeboard material subjected to axial stresses. The model permits prediction of the stress-strain curve to the ultimate stress and ultimate strain points for the material regardless of fiber alignment or board density. Comparisons are made between traditional failure criteria, experimental results, and the model predictions

Behavior of exposed column base plate connection subjected to combined axial load and biaxial bending

Column base plate (CBP) connections are one of the most crucial structural components of steel structures that act as a transfer medium for all the forces and moments from the entire building into the foundation. Importance of this type of connection becomes significant when the structure experiences dynamic loading, such as wind or earthquake, which incorporates dynamic effects in the structure that need to be transferred to the foundation. Considerable research efforts have been made over the past few decades on CBP connections, which led to the publication of AISC Design Guide 1 (2006) for CBP design. This design guide is still widely used in the industry. All the previous studies and design guidelines considered only the uniaxial (major axis) bending moment combined with axial load for CBP connection design. However, very often the base plate experiences a bidirectional bending moment from lateral loads during any dynamic loading event. Although, the column is designed and checked under combined axial load and bi-axial bending, when it comes to the base plate connection, only the axial load and major axis bending are considered. Therefore, the objective of this research is to investigate the behavior of CBP connections subjected to combined axial load and biaxial bending through an extensive numerical parametric study, using general purpose finite element software ABAQUS. For this numerical study, an accurate nonlinear finite element (FE) model is developed, considering both geometric and material nonlinearities and validated against experimental results that are available in the literature subjected to monotonic and uniaxial cyclic loading. Validation results show that the developed FE model can effectively simulate force transfer at major contact interfaces in the connection. Concurrently, a database of CBP connection subjected to axial load and uniaxial bending, is constructed from the literature to identify the influential parameters as well as different failure modes of the CBP connection, using Machine Learning (ML) approach. Among nine different ML models, the Decision tree based ML model provides an overall accuracy of 91% for identifying the failure mode whereas base plate thickness, embedment length, and anchor rod diameter are found to be the influential parameters that govern the failure mode of CBP connections. Therefore, a total of 20 different FE models that have different base plate thicknesses and yield strengths, anchor bolt sizes and quantity as well as embedment lengths, grout thicknesses and axial load ratios are developed. Furthermore, a bidirectional symmetric lateral loading protocol is developed and applied with constant axial compressive load in the developed models. The study reveals that the thickness of base plate and anchor rod diameter are the governing parameters for different base connection behavior such as moment rotation response, maximum bolt tensile force, and yield line pattern of the base plate. Moreover, the rigidity of the base plate connection is found to be in the semi-rigid region under biaxial bending condition. Finally, this study found that the available methods for uniaxial bending overpredicts the connection rotational stiffness compared to the stiffness obtained from numerical analysis considering biaxial bending

A Modeling Framework of Brittle and Ductile Fractures Coexistence in Composites

In order to reduce the weight of automobiles and aircrafts, lightweight materials, such as aluminum alloy, advanced high strength steel, composite materials, are widely used to replace the traditional materials like mild steel. Composite materials are complicated in material mechanical properties and less investigated compared to metallic materials. Engineering composites can be categorized into polymer matrix composites (PMCs), metal matrix composites (MMCs) and ceramic matrix composites (CMCs) according to their matrix materials. A set of mechanical experiments ranging from micro scale (single fiber composite and thin film composite) to macro scale (PMCs and MMCs) were conducted to fully understand the material behavior of composite materials. Loading conditions investigated includes uniaxial tension, three-point bending, uniaxial compression, simple shear, tension combined with shear, and compression combined with shear. For single fiber composite and thin-film composite, details of each composition are modelled. For the PMCs and MMCs which have plenty of reinforcements like fibers and particles, the details of the composition of structures cannot be modelled due to the current limitations of computing power. A mechanics framework of composite materials including elasticity, plasticity, failure initiation and post failure softening is proposed and applied to two types of composite materials. Uniaxial tension loading is applied to several single fiber composites and thin film composites. A surprising phenomenon, controllable and sequential fragmentation of the brittle fiber to produce uniformly sized rods along meters of polymer cladding, rather than the expected random or chaotic fragmentation, is observed with a necking propagation process. A combination of necking propagation model, fiber cracking model and interfacial model are proposed and applied to the finite element simulations. Good predictions of necking propagation and uniform fragmentation phenomenon are achieved. This modeling method of the micro-scale phenomenon reveals the physics inside composites in micro scale and helps the understanding of the process of nano fragmentation. Unidirectional carbon fiber composites were tested under multi-axial loading conditions including tensile/compression/shear loadings along and perpendicular to the fiber direction. Compression dominated tests showed a brittle fracture mode like local kicking/buckling, while tension dominated tests showed a fracture mode like delamination and fiber breakage. Simple shear tests with displacement control showed matrix material hardening and softening before total failure. The proposed modeling framework is successfully applied to the PMCs. A new parameter ψ was introduced to represent different loading conditions of PMCs. Numerical simulations using finite element method well duplicated the anisotropic elasticity and plasticity of this material. Failure features like delamination was simulated using cohesive surface feature. It is also applied to carbon fiber composite laminates to further validate the proposed model. A round of experimental study on high volume fraction of metallic matrix nano composites was conducted, including uniaxial tension, uniaxial compression, and three-point bending. The example materials were two magnesium matrix composites reinforced with 10 and 15% vol. SiC particles (50nm size). Brittle fracture mode was exhibited under uniaxial tension and three-point bending, while shear dominated ductile fracture mode (up to 12% fracture strain) was observed under uniaxial compression. Transferring the Modified Mohr Coulomb (MMC) ductile fracture model to the stress based MMC model (sMMC), the proposed modeling framework is applied to this material. This model has been demonstrated to be capable of predicting the coexistence of brittle and ductile fracture modes under different loading conditions for MMCs. Numerical simulations using finite element method well duplicated the material strength, fracture initiation sites and crack propagation modes of the Mg/SiC nano composites with a good accuracy

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone

The index mechanical properties, strength, and stiffness parameters of rock materials (i.e., uniaxial compressive strength, c, ϕ, E, and G) are critical factors in the proper geotechnical design of rock structures. Direct procedures such as field surveys, sampling, and testing are used to estimate these properties, and are time-consuming and costly. Indirect methods have gained popularity in recent years due to their time-saving and highly accurate results, which are comparable to those obtained through direct approaches. This study presents a procedure for establishing a deep learning-based predictive model (DNN) for obtaining the geomechanical characteristics of marlstone samples that have been recovered from the South Pars region of southwest Iran. The model was implemented on a dataset resulting from the execution of numerous geotechnical tests and the evaluation of the geotechnical parameters of a total of 120 samples. The applied model was verified by using benchmark learning classifiers (e.g., Support Vector Machine, Logistic Regression, Gaussian Naïve Bayes, Multilayer Perceptron, Bernoulli Naïve Bayes, and Decision Tree), Loss Function, MAE, MSE, RMSE, and R-square. According to the results, the proposed DNN-based model led to the highest accuracy (0.95), precision (0.97), and the lowest error rate (MAE = 0.13, MSE = 0.11, and RMSE = 0.17). Moreover, in terms of R2, the model was able to accurately predict the geotechnical indices (0.933 for UCS, 0.925 for E, 0.941 for G, 0.954 for c, and 0.921 for φ)

Estimation of capacity of eccentrically loaded single angle struts with decision trees

Single angle struts are used as compression members for many structures including roof trusses and transmission towers. The exact analysis and design of such members is challenging due to various uncertainties such as the end fixity or eccentricity of the applied loads. The design standards provide guidelines that have been found inaccurate towards the conservative side. Artificial Neural Networks (ANN) have been observed to perform better than the design standards, when trained with experimental data and this has been reported literature. However, practical implementation of ANN poses problem as the trained network as well as the knowhow regarding the application should be accessible to practitioners. In another data-driven tool, the Decision Trees (DT), the practical application is easier as decision based rules are generated, which are readily comprehended and implemented by designers. Hence, in this paper, DT was explored for the evaluation of capacity of eccentrically loaded single angle struts and was found to be robust and yielded comparable accuracy as ANN, and better than design code (AISC). This has enormous potential for easy and straightforward implementation by practicing engineers through the logic based decision rules, which would be easily programmable on computer. For this application, use of dimensionless ratios as inputs for the development of DT was found to yield better results when compared to the approach of using the original variables as inputs

Stochastic Multiscale Characterization of Short-Fiber Reinforced Composites

A framework for stochastic modelling and optimization of materials with engineered microstructures is presented. Numerical methods for solving problems with short-fiber inclusions are discussed. Addition of fiber reinforcement has been shown to improve the performance of various materials in a number of applications. The response of fiber reinforced concrete under tensile stress is dependent upon several properties, including fiber geometry, fiber material properties, fiber length, and orientation of the fibers with respect to the applied load. In a real-world system the distribution of the fibers may be random, with orientation angle and configuration varying locally. Stochastic multiscale methods enable the connection of the scales to analyze the effect of randomly distributed short-fiber inclusions on the global response of the system. Randomly generated characteristic volume elements (CVE) are analyzed using the extended finite element method (XFEM) to capture local material response without the need for a mesh that conforms to the material morphology, ideal for situations with arbitrary fiber distributions. The variation observed in statistically equivalent CVE models is quantified. Correlation is determined between FRC descriptor variables and the tensile response of the composite. It is demonstrated that machine learning can be used to predict composite material properties of FRC to a reasonable degree of accuracy using information about the material microstructur