预测加利福尼亚州HARHA处理的膨胀土壤的承载比,使用高斯过程回归
Mahmood Ahmad1,2, Mohammad A Al-Zubi3, Ewa Kubińska-Jabcoń4
1Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, Jalan Gombak, Selangor, 50728, Malaysia. ahmadm@iium.edu.my.
Scientific reports
|August 21, 2023
概括
这项研究引入了一种新的高斯过程回归 (GPR) 模型,以准确预测加州用水合石灰激活米灰 (HARHA) 处理的土壤的承载比率 (CBR). 该GPR模型显著优于现有的人工神经网络和基因表达编程模型.
科学领域:
- 地质技术工程 地质技术工程
- 材料科学 材料科学 材料科学
- 计算智能是一种计算智能.
背景情况:
- 加利福尼亚轴承比率 (CBR) 是评估路面设计中底层土壤强度的关键参数.
- 传统的CBR评估方法可能耗时且资源密集.
- 开发准确的CBR预测模型对于高效的道路建设至关重要.
研究的目的:
- 开发和验证一种新的高斯过程回归 (GPR) 模型,用于预测用水合石灰激活米灰 (HARHA) 处理的土壤的CBR.
- 将GPR模型的预测性能与已建立的人工神经网络 (ANN) 和基因表达编程 (GEP) 模型进行比较.
- 确定影响HARHA处理土壤CBR的关键输入参数.
主要方法:
- 使用了121个实验数据点的数据集,包括HARHA含量,液体极限,塑料极限和最佳水分含量等参数.
- 训练并开发了一种高斯过程回归 (GPR) 模型来预测CBR值.
- 使用统计指标评估模型性能:R2,MAE,RMSE,RRMSE和 ρ,并与ANN和GEP模型进行比较.
主要成果:
- 该GPR模型显示出优异的预测准确性,其中R2 = 0.9999,MAE = 0.0920,RMSE = 0.13907,RRMSE = 0.0078,以及 ρ = 0.00391.
- 在预测CBR方面,GPR模型显著超过ANN (R2 = 0.9998) 和GEP (R2 = 0.9972) 模型.
- 灵敏度分析表明,HARHA含量是影响处理土壤CBR的最有影响力的参数.
结论:
- 开发的GPR模型为预测HARHA处理土壤的CBR提供了一个高度准确和可靠的方法.
- 与ANN和GEP相比,GPR为这种特定的地质技术应用提供了更有效的计算技术.
- 这些发现凸显了HARHA含量的重要性,提高了路面底层应用的土壤强度.
更多相关视频
08:09Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
Published on: September 12, 2017
11.8K
10:25Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
Published on: June 28, 2016
10.7K
相关概念视频
01:13Resultant of a General Distributed Loading
707
While designing structures exposed to non-uniform loads, it is crucial to consider the resultant force and its location. This resultant force is a single vector representing the net force applied due to the distributed load.
Examples such as load distribution due to wind and load distribution on a bridge illustrate how this concept is used to analyze and design safe, reliable structures under variable loading conditions. Most structures, such as residential buildings, bridges, and towers, are...
Examples such as load distribution due to wind and load distribution on a bridge illustrate how this concept is used to analyze and design safe, reliable structures under variable loading conditions. Most structures, such as residential buildings, bridges, and towers, are...
707
01:20Calibration Curves: Linear Least Squares
1.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
For data that follow a straight line, the standard method for fitting is the linear...
1.4K
01:10Calibration Curves: Correlation Coefficient
1.7K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
1.7K
01:25Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
01:11Regression Analysis
5.8K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.8K
01:11Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K