Pile foundations are challenging to build due to subsurface obstacles, contractor ignorance, and difficulties with site planning. Given the unpredictable environment of the construction site, productivity losses durin...Pile foundations are challenging to build due to subsurface obstacles, contractor ignorance, and difficulties with site planning. Given the unpredictable environment of the construction site, productivity losses during pile work are to be thought possible. Prior to finishing a site pre-investigation, a foundation’s area is usually sampled for statistical reasons. There are studies on pile construction outside of Bangladesh that are supported by relevant empirical data in the literature. Since Bangladesh, which is regarded as a third-world country, is ignored in this regard, the literature currently available about pile building and the associated productivity loss is unable to provide adequate information or appropriate empirical data. Due to this pile-building sector in Bangladesh has been experiencing a decline in production for quite some time now. Before attempting to increase productivity in pile construction, it is essential to investigate the potential losses and the variables that might have an influence. This study aims to accomplish the following objectives: 1) identify the primary factors that have an impact on pile construction;2) develop an SVR model that accurately predicts productivity loss;and 3) figure out the projected loss by basing it on the historical scenario that is the most comparable to the current one. A Support Vector Regression (SVR) model was developed after a study of the relevant literature. This model enabled the collection of 110 pile building projects from five significant locations in Bangladesh. The model was constructed using a list of eight inputs in addition to a list of five macro elements (labor, management, environment, material, and equipment) (soil condition, pile type, pile material, project size, project location, pile depth, pile quantity, and equipment quantity). Using 10-way cross validation, the SVR achieves an accuracy of 87.2% in its predictions. On the basis of what has occurred in the past, we are able to estimate that there will be a loss of around 18.55 pe展开更多
文摘Pile foundations are challenging to build due to subsurface obstacles, contractor ignorance, and difficulties with site planning. Given the unpredictable environment of the construction site, productivity losses during pile work are to be thought possible. Prior to finishing a site pre-investigation, a foundation’s area is usually sampled for statistical reasons. There are studies on pile construction outside of Bangladesh that are supported by relevant empirical data in the literature. Since Bangladesh, which is regarded as a third-world country, is ignored in this regard, the literature currently available about pile building and the associated productivity loss is unable to provide adequate information or appropriate empirical data. Due to this pile-building sector in Bangladesh has been experiencing a decline in production for quite some time now. Before attempting to increase productivity in pile construction, it is essential to investigate the potential losses and the variables that might have an influence. This study aims to accomplish the following objectives: 1) identify the primary factors that have an impact on pile construction;2) develop an SVR model that accurately predicts productivity loss;and 3) figure out the projected loss by basing it on the historical scenario that is the most comparable to the current one. A Support Vector Regression (SVR) model was developed after a study of the relevant literature. This model enabled the collection of 110 pile building projects from five significant locations in Bangladesh. The model was constructed using a list of eight inputs in addition to a list of five macro elements (labor, management, environment, material, and equipment) (soil condition, pile type, pile material, project size, project location, pile depth, pile quantity, and equipment quantity). Using 10-way cross validation, the SVR achieves an accuracy of 87.2% in its predictions. On the basis of what has occurred in the past, we are able to estimate that there will be a loss of around 18.55 pe
