利用各种基于机器学习技术的模型估算橡胶化矿渣土工聚合物混凝土的抗压强度

IF 1.7 4区 工程技术 Q3 ENGINEERING, CIVIL
Sesha Choudary Yeluri, Karan Singh, Akshay Kumar, Yogesh Aggarwal, Parveen Sihag
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引用次数: 0

摘要

在追求可持续建筑实践的过程中,研究人员一直在探索替代材料,以减少混凝土生产对传统水泥的依赖。土工聚合物混凝土(GPC)因其潜在的生态效益而成为一种前景广阔的替代材料。土工聚合物混凝土混合物的配制是一项具有挑战性的任务,因为没有具体的规范条款来确定混合物的设计。确定最佳混合比例的复杂性因各种因素的影响而变得更加复杂,这些因素包括 Na2SiO3/NaOH 比率、硅酸钠 (Na2SiO3) 和氢氧化钠 (NaOH) 的用量以及不同的养护期,所有这些因素都会对混凝土的机械性能产生重大影响。在估算橡胶化矿渣基 GPC 的抗压强度时,使用了多种预测建模技术,包括多元自适应回归样条曲线 (MARS)、分组数据处理方法 (GMDH)、M5P 和线性回归 (LR)。本研究使用了一个包含 186 个观测值的数据集,其中分为包含 130 个观测值的训练数据集和包含 56 个观测值的测试数据集。研究考虑了各种输入参数,如 NaOH 摩尔比 (S)、Na2SiO3 量 (SS)、砂量 (S)、粗骨料量 (CA)、NaOH 量 (M)、铜渣量 (C)、橡胶骨料量 (RA)、固化期 (D) 和粉煤灰 (FA),并将抗压强度作为输出约束条件。使用相关系数 (CC)、纳什-苏特克利夫效率 (NSE)、平均绝对误差 (MAE)、均方根误差 (RMSE) 和分散指数 (SI) 等性能指标评估了这些方法的功效。研究结果表明,MARS 模型优于其他软计算技术,其测试 CC 为 0.9634,MAE 为 1.4509,RMSE 为 1.8465,SI 为 0.0480,NSE 为 0.9265。相反,LR 模型的性能最差,测试值 CC 为 0.8640、MAE 为 3.0411、RMSE 为 3.5375、SI 为 0.0920、NSE 为 0.7303。这些结果凸显了 MARS 作为预测橡胶化矿渣基 GPC 抗压强度的合适方法的潜力,从而带来更具可持续性的建筑方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Estimation of Compressive Strength of Rubberised Slag Based Geopolymer Concrete Using Various Machine Learning Techniques Based Models

Estimation of Compressive Strength of Rubberised Slag Based Geopolymer Concrete Using Various Machine Learning Techniques Based Models

In the quest for sustainable construction practices, researchers have been exploring alternative materials that can reduce the reliance on traditional cement in concrete production. Geopolymer concrete (GPC) has surfaced as a promising alternative due to its potential ecological benefits. The formulation of GPC mixtures is a challenging task as there is no specific code provision to determine the mix design. The complexity of determining the optimal mix proportions is compounded by the influence of various factors, including the Na2SiO3/NaOH ratio, the quantities of sodium silicate (Na2SiO3) and sodium hydroxide (NaOH), and differing curing periods, all of which significantly impact the concrete’s mechanical properties. A variety of predictive modeling techniques, including multivariate adaptive regression splines (MARS), group method of data handling (GMDH), M5P, and linear regression (LR), are used in the estimation of the compressive strength of rubberized slag-based GPC. This study utilizes a dataset comprising 186 observations, which are divided into a training dataset of 130 observations and a testing dataset of 56 observations. The investigation considers various input parameters such as the molarity of NaOH (S), Na2SiO3 quantity (SS), sand quantity (S), coarse aggregate quantity (CA), NaOH quantity (M), the quantity of copper slag (C), rubber aggregate quantity (RA), curing period (D), and fly ash (FA), with the compressive strength serving as the output constraint. The efficacy of these approaches is assessed using performance indices such as the coefficient of correlation (CC), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), and scattering index (SI). The findings indicate that the MARS model outperforms the other soft computing techniques, with a testing CC of 0.9634, MAE of 1.4509, RMSE of 1.8465, SI of 0.0480, and NSE of 0.9265. Conversely, the LR model exhibits the least favourable performance, with testing values of CC at 0.8640, MAE at 3.0411, RMSE at 3.5375, SI at 0.0920, and NSE at 0.7303. These results emphasize the potential of MARS as a suitable method for predicting the compressive strength of rubberized slag-based GPC, leading to more sustainable construction methodologies.

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来源期刊
CiteScore
3.30
自引率
11.80%
发文量
203
期刊介绍: The aim of the Iranian Journal of Science and Technology is to foster the growth of scientific research among Iranian engineers and scientists and to provide a medium by means of which the fruits of these researches may be brought to the attention of the world’s civil Engineering communities. This transaction focuses on all aspects of Civil Engineering and will accept the original research contributions (previously unpublished) from all areas of established engineering disciplines. The papers may be theoretical, experimental or both. The journal publishes original papers within the broad field of civil engineering which include, but are not limited to, the following: -Structural engineering- Earthquake engineering- Concrete engineering- Construction management- Steel structures- Engineering mechanics- Water resources engineering- Hydraulic engineering- Hydraulic structures- Environmental engineering- Soil mechanics- Foundation engineering- Geotechnical engineering- Transportation engineering- Surveying and geomatics.
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