Empirical methods for determining shaft bearing capacity of semi-deep foundations socketed in rocks

Semi-deep foundations socketed in rocks are considered to be a viable option for the foundations in the presence of heavy load imposed by high-rise structures, due to the low settlement and high bearing capacity. In the optimum design of semi-deep foundations, prediction of the shaft bearing capacity, rs, of foundations socketed in rocks is thus critically important. In this study, the unconfined compressive strength(UCS), qu, has been applied in order to investigate the shaft bearing capacity. For this, a database of 106 full-scale load tests is compiled with UCS values of surrounding rocks, in which 34 tests with rock quality designation(RQD), and 5 tests with rock mass rating(RMR). The bearing rocks for semi-deep foundations include limestone, mudstone, siltstone, shale, granite, tuff, granodiorite, claystone, sandstone, phyllite, schist, and greywacke. Using the database, the applicability and accuracy of the existing empirical methods are evaluated and new relations are derived between the shaft bearing capacity and UCS based on the types of rocks. Moreover, a general equation in case of unknown rock types is proposed and it is verified by another set of data. Since rock-socketed shafts are supported by rock mass(not intact rock), a reduction factor for the compressive strength is suggested and verified in which the effect of discontinuities is considered using the modified UCS, qu(modified), based upon RMR and RQD in order to take into account the effect of the rock mass properties.

Sparsity-Assisted Intelligent Condition Monitoring Method for Aero-engine Main Shaft Bearing

Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.

Reply to Discussion on “Empirical methods for determining shaft bearing capacity of semi-deep foundations socketed in rocks”

Semi-deep foundations socketed in rocks are considered to be a viable option for the foundations in the presence of heavy loads imposed by high-rise buildings and special structures, due to the low settlement and high bearing capacity. In this study, the unconfined compressive strength（UCS） and rock mass cuttability index（RMCI） have been applied to investigating the shaft bearing capacity. For this purpose, a comprehensive database of 178 full-scale load tests is compiled by adding a data set（n = 72）collected by Arioglu et al.（2007） to the data set（n = 106） presented in Rezazadeh and Eslami（2017）.Using the database, the applicability and accuracy of the existing empirical methods are evaluated and new relations are derived between the shaft bearing capacity and UCS/RMCI. Moreover, a general equation in case of unknown rock types is proposed and it is verified by another set of data（series 3 in Rezazadeh and Eslami（2017））. Since rock-socketed shafts are supported by rock mass（not intact rock）,a reduction factor for the compressive strength is suggested and verified in which the effect of discontinuities is considered using the modified UCS, based upon RMR and RQD to consider the effect of the rock mass properties.

Experimental validation of an integrated optimization design of a radial turbine for micro gas turbines

The aerodynamic performance, structural strength, and wheel weight are three important factors in the design process of the radial turbine for micro gas turbines. This study presents the experimental validation process of this integrated optimization design method by using the similarity theory. Cold modeling tests and investigations into the aerodynamic characteristics were performed. Experimental results showed that the aerodynamic efficiency of the micro radial turbine is 84.3% at the design point while also satisfying the aerodynamic and strength requirements. Meanwhile, the total weight of the turbine wheel is 3.8 kg which has only a 52.8% mass of the original design. This indicates that the radial turbine designed through this technique has a high aerodynamic performance, and thus can be applied to micro gas turbines. The results validated that this integrated optimization design method is reliable.