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 percent of the total output. A new perspective for engineers studying the delay factors with productivity loss is provided by the outcome of important tasks as it relates to loss in productivity and overall factors faced. In the building construction industry, effective management should place more emphasis on the correlation between productivity loss and the factors that cause it. Therefore, to represent the effect on productivity loss, real factors can be summed up as a decline in productivity loss. The findings of the study would urge specialists to concentrate on waste as a means of increasing overall production.展开更多
Low-resistivity oil layers are often missed in logging interpretation because of their resistivity close to or below the resistivity of nearby water layers. Typical low-resistivity oil layers have been found in the pa...Low-resistivity oil layers are often missed in logging interpretation because of their resistivity close to or below the resistivity of nearby water layers. Typical low-resistivity oil layers have been found in the past few years in the Putaohua reservoir of the Puao Oilfield in the south of the Daqing placanticline by detailed exploration. Based on a study of micro-geological causes of low-resistivity oil layers, the macro-geological controlling factors were analyzed through comprehensive research of regional depositional background, geological structure, and oil-water relations combined with core, water testing, well logging, and scanning electron microscopy data. The results showed that the formation and distribution of Putaohua low-resistivity oil layers in the Puao Oilfield were controlled by depositional environment, sedimentary facies, diagenesis, motive power of hydrocarbon accumulation, and acidity and alkalinity of reservoir liquid. The low-resistivity oil layers caused by high bound-water saturation were controlled by deposition and diagenesis, those caused by high free-water saturation were controlled by structural amplitude and motive power of hydrocarbon accumulation. Those caused by formation water with high salinity were controlled by the ancient saline water depositional environment and faulted structure and those caused by additional conductivity of shale were controlled by paleoclimate and acidity and alkalinity of reservoir liquid. Consideration of both micro-geological causes and macro-geological controlling factors is important in identifying low-resistivity oil layers.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 percent of the total output. A new perspective for engineers studying the delay factors with productivity loss is provided by the outcome of important tasks as it relates to loss in productivity and overall factors faced. In the building construction industry, effective management should place more emphasis on the correlation between productivity loss and the factors that cause it. Therefore, to represent the effect on productivity loss, real factors can be summed up as a decline in productivity loss. The findings of the study would urge specialists to concentrate on waste as a means of increasing overall production.
基金supported by the National Natural ScienceFoundation Project(No.40173023)
文摘Low-resistivity oil layers are often missed in logging interpretation because of their resistivity close to or below the resistivity of nearby water layers. Typical low-resistivity oil layers have been found in the past few years in the Putaohua reservoir of the Puao Oilfield in the south of the Daqing placanticline by detailed exploration. Based on a study of micro-geological causes of low-resistivity oil layers, the macro-geological controlling factors were analyzed through comprehensive research of regional depositional background, geological structure, and oil-water relations combined with core, water testing, well logging, and scanning electron microscopy data. The results showed that the formation and distribution of Putaohua low-resistivity oil layers in the Puao Oilfield were controlled by depositional environment, sedimentary facies, diagenesis, motive power of hydrocarbon accumulation, and acidity and alkalinity of reservoir liquid. The low-resistivity oil layers caused by high bound-water saturation were controlled by deposition and diagenesis, those caused by high free-water saturation were controlled by structural amplitude and motive power of hydrocarbon accumulation. Those caused by formation water with high salinity were controlled by the ancient saline water depositional environment and faulted structure and those caused by additional conductivity of shale were controlled by paleoclimate and acidity and alkalinity of reservoir liquid. Consideration of both micro-geological causes and macro-geological controlling factors is important in identifying low-resistivity oil layers.
