in geotechnical engineering, foundation piles are ideal for deep foundations that cannot bear higher loads. This architectural expansion places a great deal of responsibility on the engineer to anticipate the appropriate load for the constructor. Unfortunately, calculations of the pile’s bearing capacity are not accessible. It has always been a source of concern for geotechnical engineers, as the structure’s safety depends on the pile’s bearing capacity and gives it a safe value. These research tests are previously known pile load test data from several locations in Nasiriyah to determine the ultimate load-carrying capacity using various interpreting methodologies. A database that was used to test the pile load for three different areas in Nasiriyah, southern Iraq: The Main Drain River Bridge Project, the Al-Eskan Interchange Project, and the Al-Hawra Hospital, as determined by analytical methods, as well as evaluating the final loading values resulting from the methods used, by ASTM D-1143, American and British Standard Code of Practice BS 800. The final capacity for the pile bearing is estimated using these approaches, which are depicted in the form of a graph-based on field data. Chin-Kondner and Brinch Hansen algorithms anticipate the highest failure load for all piles based on the comparison. On average, Chin–Kondner’s ultimate load is 22% higher than Hansen’s maximum load for the 22 pile load tests. Decourt and DeBeer, and Mazurkiewicz’s techniques yielded the closest average failure load. Buttler-Hoy approach yielded the smallest failure load.

No abstract

The identification and stratification of soils represent an essential step in designing various geotechnical structures. The cone penetration test (CPT) measurements are used widely to classify the soil; however, the soil classification charts such as the Robertson chart undergo uncertainty from different sources that make overlapping of soil types. This article aims to develop a probabilistic approach employing clustering with Gaussian mixture model, which can deal with the uncertainty and classify the soil based on CPT. The spatial parameters were obtained assuming the different types of covariance matrices. The data utilized in this study represent the results of CPT in four locations in Nasiriyah, Iraq. Both spatial and feature patterns were produced and used to classify the soil. This research revealed that the soils deduced from the Robertson chart were clay, silt, and sand. No gravelly sand appeared on the chart. The soil at shallow depth was clay soils, and it changed to be sandy silt at fairly great depth. They were close to the boundary curve between the stiff clay and sand zones and relatively existed at great depth. The probabilistic approach can detect the soil layers fast without experience-based decisions. Moreover, the type of assumed covariance matrix may affect the soil profile.