Recently, Falling Weight Deflectometer (FWD) is one of the most significant testing used to measure the surface deflections under impact load subjected to circle plate which was used to back-calculating elastic moduli of the road pavement layer. Several back-calculation programs are useful for back-calculating road pavement layer moduli. A genetic algorithm (GA) was used successfully in this problem but it requires more computation time to a variation of computed deflection using optimized moduli value of road pavement layer based on the GA. There is a few research adopted to Artificial Neural Network (ANN) for computing deflection of the road pavement system. This article aimed to develop ANN for computing surface deflection of pavement using layer moduli and its thicknesses as input parameters. We have also discussed the solution techniques and algorithms for use in developing the program, including Burmister theory for determining deflection based on a cylindrical coordinate system, the GA optimization for back-calculating road pavement moduli, and the development of the ANN-GA model. The evaluation shows that the predicted deflections using the ANN compare well with computed deflections from the hypothetical model. Backcalculated layer moduli based on the GA-ANN model are well with a hypothetical model based on FWD test.

Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R² = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.

A rover is a planetary surface exploration device designed to move across the ground on a planet or a planetary-like body. Exploration rovers are increasingly becoming a vital part of the search for scientific evidence and discoveries on a planetary satellite of the Sun, such as the Moon or Mars. Reliable behavior and predictable locomotion of a rover is important. Understanding soil behavior and its interaction with rover wheels—the terramechanics—is of great importance in rover exploration performance. Up to now, many researchers have adopted Bekker’s semiempirical model to predict rover wheelsoil interaction, which is based on the assumption that soil is deformable when a pressure is applied to it. Despite this basic assumption of the model, the pressure-sinkage relation is not fully understood, and it continues to present challenges for rover designers. This article presents a new pressure-sinkage model based on dimensional analysis (DA) and results of bevameter tests. DA was applied to the test results in order to propose a new pressure-sinkage model by reducing physical quantitative parameters. As part of the work, a new bevameter was designed and built so that it could be successfully used to obtain a proper pressure-sinkage relation of Korean Lunar Soil Simulant (KLS-1). The new pressure-sinkage model was constructed by using three different sizes of flat plate diameters of the bevameter. The newly proposed model was compared successfully with other models for validation purposes.

Most of the structures that are damaged by an explosion are not initially designed to resist this kind of load. In the overall structure of any building, columns play an important role to prevent the collapse of frame structure under blast impact. Hence, the main concept in the blast resistance design of the structure is to improve the blast load capacity of the column. In this study, dynamic analysis and numerical model of Ultra High Performance Concrete (UHPC) column under high explosive load, is presented. Based on the Johnson Holmquist 2 damage model and the subroutine in the ABAQUS platform, a total of twenty UHPC model of the column were calculated. The objective of the article is to investigate the structural response of the UHPC column and locate the most vulnerable scenarios to propose necessary recommendations for the UHPC column in the blast loading resistance design. The input parameters, including the effect of various shapes of cross-section, scaled distance, steel reinforcement ratio, and cross-section area, are analyzed to clarify the dynamic behavior of the UHPC column subjected to blast loading. Details of the numerical data, and the discussion on the important obtained results, are also provided in this paper.

This study introduces a Multi-Depth Deflectometer (MDD) combined with a Global Navigation Satellite System (GNSS) which was developed for measuring the deformation of railway slab track layers. We newly designed the MDD with the addition of laser sensor modules for increasing precision in which the MDD head is fixed at the top of the track slab. The MDD/GNSS system can measure the relative deflection between each track layer as well as the total deflection at the top of the track slab, which makes it possible to evade the fixed condition problem of classical MDD. The new MDD/GNSS system was installed at the transition zone between a tunnel entrance and its embankment, which experienced high settlement levels prior to repair. The system was used to monitor whether the repaired concrete track foundation with pressurized cement grouting was stabilized effectively and what track layer position was most unstable so that it had the most influence. The GNSS system was designed and built for computing net settlement at each track layer even when the MDD could not be fixed firmly at the end point, which is a major drawback of classical MDD. The results obtained from MDD and GNSS measurements indicated significant potential in aiding railway track settlement measurements.