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
PM_(2.5)对大气环境和人类健康危害极大,及时准确地掌握高时空分辨率的PM_(2.5)浓度对空气污染防治起着重要作用.基于粤港澳大湾区2015~2020年多角度大气校正算法(MAIAC)1 km AOD产品、ERA5气象资料和站点污染物浓度(CO、O_(3)、NO_(2)...PM_(2.5)对大气环境和人类健康危害极大,及时准确地掌握高时空分辨率的PM_(2.5)浓度对空气污染防治起着重要作用.基于粤港澳大湾区2015~2020年多角度大气校正算法(MAIAC)1 km AOD产品、ERA5气象资料和站点污染物浓度(CO、O_(3)、NO_(2)、SO_(2)、PM10和PM_(2.5)),分别建立了估算PM_(2.5)浓度的时空地理加权模型(GTWR)、BP神经网络模型(BPNN)、支持向量机回归模型(SVR)和随机森林模型(RF).结果表明,RF模型的估算能力优于BPNN、SVR和GTWR模型,BPNN、SVR、GTWR和RF模型的相关系数依次为0.922、0.920、0.934和0.981,均方根误差(RMSE)分别为7.192、7.101、6.385和3.670μg·m^(-3),平均绝对误差(MAE)分别为5.482、5.450、4.849和2.323μg·m^(-3);RF模型在季节PM_(2.5)的预测中以冬季效果最佳、夏季次之、春季和秋季再次,预测值与实测值的相关系数在0.976以上;RF模型可用于大湾区PM_(2.5)浓度的预测分析研究.在时间上,大湾区各市2021年逐日ρ(PM_(2.5))呈“先减后增”的变化趋势,最高值在65.550~112.780μg·m^(-3),最低值介于5.000~7.899μg·m^(-3);月均浓度变化呈“U”型分布,1月开始降低至6月达到谷值后逐渐升高;季节上表现为冬季浓度最高、夏季最低、春秋季节过渡的特点;大湾区年均ρ(PM_(2.5))为28.868μg·m^(-3),低于年均二级浓度限值.空间上,2021年PM_(2.5)呈“西北-东南”递减的特征,高污染区域聚集在大湾区的中部,以佛山为代表;低浓度区主要分布在惠州东部、港澳和珠海等沿海地区;不同季节PM_(2.5)浓度在空间分布上也表现出异质性和区域性.RF模型估算了高精度PM_(2.5)浓度,为大湾区PM_(2.5)污染相关的健康风险评估提供了科学依据.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
文摘PM_(2.5)对大气环境和人类健康危害极大,及时准确地掌握高时空分辨率的PM_(2.5)浓度对空气污染防治起着重要作用.基于粤港澳大湾区2015~2020年多角度大气校正算法(MAIAC)1 km AOD产品、ERA5气象资料和站点污染物浓度(CO、O_(3)、NO_(2)、SO_(2)、PM10和PM_(2.5)),分别建立了估算PM_(2.5)浓度的时空地理加权模型(GTWR)、BP神经网络模型(BPNN)、支持向量机回归模型(SVR)和随机森林模型(RF).结果表明,RF模型的估算能力优于BPNN、SVR和GTWR模型,BPNN、SVR、GTWR和RF模型的相关系数依次为0.922、0.920、0.934和0.981,均方根误差(RMSE)分别为7.192、7.101、6.385和3.670μg·m^(-3),平均绝对误差(MAE)分别为5.482、5.450、4.849和2.323μg·m^(-3);RF模型在季节PM_(2.5)的预测中以冬季效果最佳、夏季次之、春季和秋季再次,预测值与实测值的相关系数在0.976以上;RF模型可用于大湾区PM_(2.5)浓度的预测分析研究.在时间上,大湾区各市2021年逐日ρ(PM_(2.5))呈“先减后增”的变化趋势,最高值在65.550~112.780μg·m^(-3),最低值介于5.000~7.899μg·m^(-3);月均浓度变化呈“U”型分布,1月开始降低至6月达到谷值后逐渐升高;季节上表现为冬季浓度最高、夏季最低、春秋季节过渡的特点;大湾区年均ρ(PM_(2.5))为28.868μg·m^(-3),低于年均二级浓度限值.空间上,2021年PM_(2.5)呈“西北-东南”递减的特征,高污染区域聚集在大湾区的中部,以佛山为代表;低浓度区主要分布在惠州东部、港澳和珠海等沿海地区;不同季节PM_(2.5)浓度在空间分布上也表现出异质性和区域性.RF模型估算了高精度PM_(2.5)浓度,为大湾区PM_(2.5)污染相关的健康风险评估提供了科学依据.
