Journal of Surveying Engineering

The most common type of horizontal curves used to connect intersecting straight sections of highways and other infrastructures are circular curves. Given the radius and the deflection angle of a horizontal circular curve, the other curve elements can be explicitly determined. However, there are 10 practical situations in which these parameters are unknown, and they have to be determined from two other given elements. In the present paper, these cases are classified into three groups according to their solution type. In the first group, direct analytical solutions can be easily obtained using simple algebraic operations. In the second group, full analytical solutions with physical meaning are not available, and in the third group, existing methods rely on a tedious trial procedure for most cases. The paper develops noniterative exact solutions for the two cases of the second group and noniterative near-exact solutions for the four cases of the third group. The proposed near-exact solutions for the third group, which have a maximum percentage error less than $2.5\times {10}^{-4}\%$, facilitate the design and analysis of horizontal circular curves.

The paper presents a simple method of finding the solution to the planar three point resection problem. The main concept leading to the solution is based on an idea of two intersecting circles (which is not new in the literature). The points of intersection of two circles (of which one solves the problem) are obtained by solving a quadratic equation. As a result of the fact that one root of the quadratic equation is known, Vieta’s formula is applied to find the other. When one of the measured angles is equal to 0 or 180°, the problem reduces to the intersection of a straight line and a circle. This also leads to a quadratic equation which is solved by Vieta’s formula. The derivation of the method is very simple (purely analytic) and free from any intermediate parameters, for example, angles, distances, or azimuths.

The Houston area has been suffering from subsidence for several decades. Therefore, continuously operating reference stations (CORS) in this area may have experienced considerable vertical displacements. The Online Positioning User Service (OPUS), provided by the National Geodetic Survey (NGS), uses CORS as references in its data processing. This study investigated what effects, if any, the subsidence experienced by these CORS around Houston contributes to the accuracy of OPUS vertical results. Our OPUS results were determined from three long-term (over 10-year) blocks of continuous data using Global Positioning System (GPS) stations located in different parts of the Houston area. The OPUS results were compared with the vertical measurements from the precise point positioning with single receiver phase ambiguity (PPP-SRPA) solution implicit in the GIPSY/OASIS 6.1.2 software. This particular study indicates that OPUS achieves 1.0-cm vertical accuracy for daily sessions (24 h) in the region around Houston, which is comparable to the OPUS accuracy previously reported in other investigations. Subsidence as slow as $0.5\hspace{0.17em}\text{cm}/\text{year}$ can be detected by analyzing OPUS results spanning 5 or more years of data. Our main conclusion is that the subsidence experienced by NGS CORS in the Houston area does not considerably affect the accuracy of OPUS vertical results. For those users who require a few centimeters of vertical accuracy for daily sessions, OPUS is a good choice, because users do not need to establish any control stations and do not need to install any GPS software packages on their local computers.

Feature conjugation is a major task in modern-day spatial analysis and contributes to efficient integration across multiple data sets. In this study, an efficient approach that utilizes the intensity information provided in most light detection and ranging (LIDAR) data sets for feature conjugation is proposed. First, a two-dimensional (2D) intensity map is generated based on the original intensity-coded LIDAR observables in three-dimensional (3D) space. The 2D map is further transformed into a regularly sampled image, and an image feature detection technique is subsequently applied to identify point conjugations between a pair of intensity maps. Finally, the paired conjugations in the image space are mapped backward into the LIDAR space, and the object coordinates of the conjugate points can be verified and obtained. Based on the numerical results from a real world case study, it is illustrated that by fully exploring the existing spectral information, a reliable feature conjugation across multiple LIDAR data sets can be easily achieved in an efficient and automatic manner.

Periodic structural health monitoring of infrastructure systems is important to avoid economic losses and human casualties. Traditionally, deformation monitoring has been done through surveying techniques. Recently, with the increased availability of inexpensive off-the-shelf cameras, photogrammetry has become a viable noncontact alternative for complete three-dimensional reconstruction of the object or surface of interest. This paper aims at combining two methodologies of photogrammetric reconstruction—image-matching-based reconstruction and model-based image fitting—to achieve submillimeter precision for the estimation of both vertical deflections and horizontal displacements. The proposed methodology was tested with data collected using a photogrammetric system at a structures laboratory where a concrete beam was subjected to different loading conditions by a hydraulic actuator. The experimental results showed that the photogrammetric system was capable of monitoring both static and dynamic deformations. The methodology used exhibited a high level of automation and the final results yielded a root-mean-square error (RMSE) of half a millimeter.