A new machine learning method has been developed to accurately predict the compaction density of earth-rock dams. This method uses a portable falling weight deflectometer (PFWD) to measure the time history signal of the dam, which is then analyzed and processed using machine learning algorithms to extract six characteristic parameters. These parameters are then used to predict the wet density and dry density of the dam materials. The method was evaluated using a case study of a dam, and the results showed that the root-mean-square error (RMSE) of the predicted wet density was within 0.041 g/cm3 and that of the predicted dry density was within 0.032 g/cm3. This demonstrates that the new method is an accurate and effective way to rapidly detect and control the construction quality of earth-rock dams.
Gram-negative bacteria, prepared on a nickel plate without additives, were examined for their intrinsic capacitance. Varying electrode mass loadings were used to assess morphological and electrochemical properties. After 5000 cycles at 1 A∙g−1 current density, the sample exhibited a specific capacitance of 37F∙g−1, demonstrating potential for energy storage devices and fundamental research on bacteria-doped composites.
An artificial intelligence (AI) system has found telltale arcs of light in deep space that could be evidence of new black holes or neutron stars. The AI, called Einstein@Home, analyzed data from the European Southern Observatory’s Very Large Telescope (VLT) and found the arcs in images of distant galaxies. The arcs are caused by the […]
Ideal weight not only impacts the overall personality of a person but at the same time, is also the hallmark of good health. Weight management is often goggled in view of its various implications, mostly in the form of grave lifestyle associated disorders. Obesity implication in early lives: The obesity statistics in India are alarming, […]