revise paragraph as scientific writing 'The accurate prediction of pile behaviors in complex soil conditions is of paramount importance in the successful design NAME_1 construction of deep foundation systems. In the region of Bangkok, the subsoil characteristics present considerable challenges due to the presence of alternating clay NAME_1 sand layers, which exhibit high spatial variability NAME_1 compressibility. Addressing these geotechnical complexities requires the application of advanced computational techniques to precisely forecast pile responses under varying loads. Traditionally, unforeseen pile deformation in Bangkok's subsoil necessitates costly pile loading tests in the field, involving static or dynamic testing, to ensure NAME_1 predict the pile's response to incremental loading. Consequently, a substantial volume of pile load test data is available for Bangkok's subsoil. However, developing an innovative method to predict pile behaviors under increasing loading conditions could prove highly beneficial. Currently, several methods are being explored to predict pile behaviors in Bangkok's subsoil. These methods encompass analytical approaches, Finite Element Analysis (FEA) (Likitlersuang NAME_2 al. 2018, Phoban NAME_2 al. 2021), NAME_1 deep learning techniques (NAME_3 NAME_1 NAME_4 2017). Each approach brings its unique set of advantages NAME_1 limitations, requiring further investigation to ascertain the most accurate NAME_1 efficient prediction method for pile behaviors in this challenging soil environment.
With the advent of the big data era, deep learning (DL) has garnered substantial attention in the realm of artificial intelligence (AI) research. In comparison to conventional machine learning (ML) methods, DL algorithms exhibit superior feature learning NAME_1 expression capabilities, driving researchers from diverse disciplines to explore its burgeoning applications. Notably, several studies have utilized deep learning techniques to investigate pile behaviors, with various researchers contributing to this domain (NAME_3 NAME_1 NAME_4 2017, Alzo’ubi NAME_1 NAME_5 2019, 2019, NAME_6 NAME_2 al. 2020a, 2020b, NAME_7 2021, NAME_8 NAME_2 al. 2023). The majority of these studies have employed fully connected neural networks to predict the load settlement curve of piles. These neural networks incorporate important pile properties, such as diameter NAME_1 length, as input parameters. In some cases, attempts have been made to include soil profiles as additional features to train the network using fully connected architecture ((Alzo’ubi NAME_1 NAME_5 2019), along with p Avoid using the following keywords: writing, variability, name_7, layer
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