Side Resistance of Drilled Shafts Socketed into Rocks for Serviceability and Ultimate Limit States

This study evaluates the analysis models of side resistance in rock sections by utilizing a wide variety of load test data. Available analytical models including the empirical adhesion factor versus the rock’s uniaxial compressive strength and its root are analyzed and compared statistically to determine the optimum relationships. The interpretation criteria for the L1 and L2 methods are used to analyze the load test results for serviceability and ultimate limit states, respectively. The analysis results show that the relationship model with the empirical adhesion factor versus the root of the rock’s uniaxial compressive strength exhibits better correlation than the one with the rock’s uniaxial compressive strength. Moreover, the general coordinate axes regression equation demonstrates better reliability than the semi-logarithmic and full logarithmic axes equations for both limit states. Based on these analyses, specific design recommendations for the side resistance of drilled shafts socketed into rocks are developed and provided with the appropriate statistics to verify their reliability

Improvement of LiDAR Data Accuracy Using 12 Parameter Affine Transformation

LiDAR data in a local coordinate system may need to be georeferenced and converted into a geographic or projected system. In coordinate transformation, the 7-parameter Helmet transformation method is usually used in measurements to eliminate the systematic errors made by a laser scanner. However, 7-parameter coordinate transformation assumes that there is only one scale error in all of the systematic errors. This study used 12 parameter affine transformation for coordinate transformation of airborne LiDAR data and terrestrial LiDAR data. The LiDAR data accuracy results upon 6-parameter similarity transformation, 7-parameter similarity transformation, and 12-parameter affine transformation were compared. The results showed that using 12-parameter affine transformation the airborne LiDAR and terrestrial LiDAR data have 2-3 times greater accuracy than do 7-parameter or 6-parameter transformations

Statistical model of OFDMA cellular networks uplink interference using lognormal distribution

In this letter, we propose an analytical approach to model uplink intercell interference (ICI) in hexagonal grid based orthogonal frequency division multiple access (OFMDA) cellular networks. The key idea is that the uplink ICI from individual cells is approximated with a lognormal distribution with statistical parameters being determined analytically. Accordingly, the aggregated uplink ICI is approximated with another lognormal distribution and its statistical parameters can be determined from those of individual cells using Fenton-Wilkson method. Analytic expressions of uplink ICI are derived with two traditional frequency reuse schemes, namely integer frequency reuse schemes with factor 1 (IFR-1) and factor 3 (IFR-3). Uplink fractional power control and lognormal shadowing are modeled. System performances in terms of signal to interference plus noise ratio (SINR) and spectrum efficiency are also derived. The proposed model has been validated by simulations