چكيده به لاتين
Inadequate compaction percentage is one of the important causes of premature road damage. Compaction operation is an important process that makes the materials of different layers of embankment and road surface obtain sufficient compaction. In the construction of the highway bed, the eligibility or not of the degree of compaction is an important criterion to judge the quality of road construction. Compaction makes the soil bed reach the required standard compaction percentage to improve the carrying capacity of the road. Improving the degree of bed compaction helps to prevent bed settlement, reduce moisture infiltration in the road bed and increase the durability of the bed. Therefore, adopting effective methods to detect the percentage of compaction is an important tool for monitoring it. One of the main challenges of road construction projects is the timely, real-time and uniform diagnosis of soil density, which is currently done by performing traditional tests such as sand cones, which due to the distance of about 50 meters determined in the regulations, for two samples consecutively, it cannot be a suitable representative for the entire 50-meter route, and on the one hand, there may be biases in the selection of test points, and on the other hand, it requires physical presence at the site, and conducting the test is time-consuming, and the implementation of the project may be delayed. slow. The purpose of this research is to solve the aforementioned challenges by using new generation sensors that can be obtained in Iran and to establish a logical connection between the data measured by these sensors during compaction operations and the results of soil compaction tests on site and in The Internet of Things platform was implemented so that soil density can be determined in real-time, continuously and from any point outside the construction site without human intervention. For this purpose, 5 types of accelerometer sensors were tested in order to select the most suitable and optimal sensor for the final device. The effective value of accelerometer data was determined by sand cone test according to each density pass and after drawing the regression diagram in two linear and non-linear modes, formulas were obtained to calculate the percentage of soil density in terms of acceleration. After building and calibrating the device in order to determine the accuracy and accuracy of measuring the formulas for determining the percentage of soil density, data collection was done again with the built device, in a specific route of the road, and the results were obtained with the results. From the sand cone test, it was compared that the errors obtained in the case without removing outlier data, in the case of the lowest error was 0.09% and the highest error was 7.1%, which shows the high accuracy of the measurement. Since the highest possible density percentage is usually 100%, therefore, the percentage of densities greater than 100% is considered as outlier data and the equivalent accelerations greater than this number were removed from the calculations, and again the density percentage calculated with the device with the results of the sand cone test. It has been compared that in this mode, the lowest error is 0.08% and the highest error is 4.25%, which is more accurate than the mode without outlier data removal. Finally, in order to monitor in real-time and without being present at the workshop, the manufactured device was connected to one of the Internet of Things platforms, and by transferring the data of the device wirelessly to the cloud, it became possible to control the percentage of soil density from any other place.
