Khan Kaffayatullah, Jalal Fazal E, Khan Mohsin Ali, Salami Babatunde Abiodun, Amin Muhammad Nasir, Alabdullah Anas Abdulalim, Samiullah Qazi, Arab Abdullah Mohammad Abu, Faraz Muhammad Iftikhar, Iqbal Mudassir
Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Materials (Basel). 2022 Jun 21;15(13):4386. doi: 10.3390/ma15134386.
Stabilized aggregate bases are vital for the long-term service life of pavements. Their stiffness is comparatively higher; therefore, the inclusion of stabilized materials in the construction of bases prevents the cracking of the asphalt layer. The effect of wet−dry cycles (WDCs) on the resilient modulus (Mr) of subgrade materials stabilized with CaO and cementitious materials, modelled using artificial neural network (ANN) and gene expression programming (GEP) has been studied here. For this purpose, a number of wet−dry cycles (WDC), calcium oxide to SAF (silica, alumina, and ferric oxide compounds in the cementitious materials) ratio (CSAFRs), ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ3), and deviator stress (σ4) were considered input variables, and Mr was treated as the target variable. Different ANN and GEP prediction models were developed, validated, and tested using 30% of the experimental data. Additionally, they were evaluated using statistical indices, such as the slope of the regression line between experimental and predicted results and the relative error analysis. The slope of the regression line for the ANN and GEP models was observed as (0.96, 0.99, and 0.94) and (0.72, 0.72, and 0.76) for the training, validation, and test data, respectively. The parametric analysis of the ANN and GEP models showed that Mr increased with the DMR, σ3, and σ4. An increase in the number of WDCs reduced the Mr value. The sensitivity analysis showed the sequences of importance as: DMR > CSAFR > WDC > σ4 > σ3, (ANN model) and DMR > WDC > CSAFR > σ4 > σ3 (GEP model). Both the ANN and GEP models reflected close agreement between experimental and predicted results; however, the ANN model depicted superior accuracy in predicting the Mr value.
稳定粒料基层对路面的长期使用寿命至关重要。它们的刚度相对较高；因此，在基层施工中加入稳定材料可防止沥青层开裂。本文研究了干湿循环（WDC）对用CaO和胶凝材料稳定的路基材料回弹模量（Mr）的影响，采用人工神经网络（ANN）和基因表达式编程（GEP）进行建模。为此，将若干干湿循环次数（WDC）、氧化钙与SAF（胶凝材料中的二氧化硅、氧化铝和氧化铁化合物）的比例（CSAFR）、最大干密度与最佳含水量的比值（DMR）、围压（σ3）和偏应力（σ4）作为输入变量，将Mr作为目标变量。使用30%的实验数据开发、验证和测试了不同的ANN和GEP预测模型。此外，还使用统计指标对它们进行了评估，如实验结果与预测结果之间回归线的斜率以及相对误差分析。ANN和GEP模型回归线的斜率在训练、验证和测试数据中分别为（0.96、0.99和0.94）以及（0.72、0.72和0.76）。ANN和GEP模型的参数分析表明，Mr随DMR、σ3和σ4的增加而增加。干湿循环次数的增加会降低Mr值。敏感性分析表明重要性顺序为：DMR > CSAFR > WDC > σ4 > σ3（ANN模型）和DMR > WDC > CSAFR > σ4 > σ3（GEP模型）。ANN和GEP模型都反映了实验结果与预测结果之间的密切一致性；然而，ANN模型在预测Mr值方面表现出更高的准确性。
