Ring foundations are widely used to support various types of offshore structures because of the cost-effective reason. Unlike the traditional circular foundations, very few studies were reported with regard to the bearing capacity of ring foundations. In addition, the study on the stability effects of anisotropic and nonhomogeneous clays is yet to be found in the current literature. The purpose of the technical note is therefore to bridge the current research gap. By using the robust lower and upper bound finite-element limit analysis in axisymmetric conditions, new stability solutions of ring footings on anisotropic and nonhomogeneous clays are obtained using dimensionless parameters. The associated failure mechanisms are also examined and discussed, while the design equations are proposed for predicting the bearing capacity of ring foundations. The findings in this study provide a reliable solution to improve the current design standard for ring footings in anisotropic and nonhomogeneous clays.

This study aims to explore the tensile and impact properties (tensile strength, modulus of elasticity, and impact strength) of polypropylene (PP)-based nanocomposites reinforced with graphene nanosheets, nanoclay, and basalt fibers. The response surface methodology (RSM) with Box–Behnken design (BBD) was adopted as the experimental design. An internal mixer was used to prepare compounds consisting of 0, 0.75 and 1.5 wt% graphene nanosheets, 0, 10 and 20 wt% basalt fibers, and 0, 3 and 6 wt% nanoclay. The samples were prepared by a hot press machine for mechanical testing. The tensile tests were run to determine the tensile strength, and modulus of elasticity, and the Charpy impact tests were performed to assess the impact strength. It was found that the addition of basalt increased the tensile strength, modulus of elasticity, and impact strength by 32%, 64% and 18%, respectively. Also, the incorporation of the low-weight graphene nanosheets increased the tensile and impact strength by 15% and 20%, respectively, Adding graphene nanosheets generally improved the modulus of elasticity by 66%. Similarly, the addition of nanoclay improved the tensile strength by 17% and increased the modulus of elasticity by 59%, but further addition of it decreased the impact strength by 19%. The values obtained by this experiment for the mechanical property were roughly close to the data yielded from desirability optimization.

To examine the acousto-structural behavior of a sandwich cylindrical shell benefiting from hexagonal honeycomb structures in its core and functionally graded porous (FGP) layers on its outer and inner surfaces, a comprehensive study based on an analytical model which also considers the effect of an external flow is conducted. A homogenous orthotropic model is used for the honeycomb core while its corresponding material features are found from the modified Gibson’s equation. The distribution pattern of FGP parts is either even or logarithmic-uneven, and a special rule-of-mixture relation governs their properties. Based on the first-order shear deformation theory (FSDT), Hamilton’s principle is exploited to derive the final coupled vibro-acoustic equations, which are then solved analytically to allow us to calculate the amount of sound transmission loss (STL) through the whole structure. This acoustic property is further investigated in the frequency domain by changing a set of parameters, i.e., Mach number, wave approach angle, structure’s radius, volume fraction, index of functionally graded material (FGM), and different honeycomb properties. Overall, good agreement is observed between the result of the present study and previous findings.

This research aimed to investigate the effectiveness of Bacillus subtilis (B. subtilis) in self-healing cracks in concrete and enhancing concrete strength through microbial induced calcium carbonate precipitation (MICP). The study evaluated the ability of the mortar to cover cracks within 28 days, taking into account the width of the crack, and observed the recovery of strength after self-healing. The use of microencapsulated endospores of B. subtilis was also examined for its impact on the strength of concrete. The compressive, splitting tensile, and flexural strengths of normal mortar were compared to those of biological mortar, and it was found that biological mortar had a higher strength capacity. Microstructure analysis using SEM and EDS showed that bacterial growth increased calcium production, contributing to the improved mechanical properties of the bio-mortar.

In this paper, Artificial Neural Networks (ANN) have been utilized to predict the stability of a planar tunnel heading in rock mass based on the well-defined Hoek-Brown (HB) yield criterion. The HB model was developed to capture the failure criterion of rock masses. To provide the datasets for an ANN model, the numerical upper bound (UB) and lower bound (LB) solutions obtained from the finite element limit analysis (FELA) with the HB failure criterion for the problem of tunnel headings are derived. The sensitivity analysis of all influencing parameters on the stability of rock tunnel heading is then performed on the developed ANN model. The proposed solutions will enhance the dependability and preciseness of predicting the stability of rock tunnel heading. Note that the effect of the unlined length ratio has not been explored previously but has been found to be of critical importance and significantly contributes to the failure of rock tunnel heading. By utilizing the machine learning-aided prediction capability of the ANN approach, the numerical solutions of the stability of tunnel heading can be accurately predicted, which is better than the use of the classic linear regression approach. Thus, providing a better and much safer assessment of mining or relatively long-wall tunnels in rock masses.

In this paper, artificial neural network (ANN) models are presented in order to enable a prompt assessment of the stability factor of tunnels in rock masses based on the Hoek–Brown (HB) failure criterion. Importantly, the safety assessment is one of the serious concerns for constructing tunnels and requires a reliable and accurate stability analysis. However, it is challenging for engineers to construct finite element limit analysis (FELA) algorithms with the HB failure criterion for tunnel stability solutions in rock masses. For the first time, a machine-learning-aided prediction of tunnel stability based on the HB failure criterion is proposed in this paper. Three different shapes of tunnels, i.e., heading tunnel, dual square tunnels, and dual circular tunnels, are considered. The inputs include four dimensionless parameters for the heading tunnel including the cover-depth ratio, the normalized uniaxial compressive strength, the geological strength index (GSI), and the m_i parameter. Moreover, dual square and circular tunnels include one more additional parameter namely the distance ratio. The results present the best ANN models for each tunnel shape, providing very reliable solutions for predicting the tunnel stability based on the HB failure criterion.