智能生物混凝土抗压强度预测的机器学习和RSM模型

IF 2.1 3区 工程技术 Q2 ENGINEERING, CIVIL
H. A. Algaifi, Suhaimi Abu Bakar, Rayed Alyousef, A. M. Sam, Ali S Alqarni, M. Ibrahim, S. Shahidan, M. Ibrahim, B. Salami
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引用次数: 11

摘要

近年来,基于细菌的自修复混凝土已被广泛开发,以使用不同的细菌种类来提高混凝土的抗压强度。然而,最优反应参数的确定和理论框架信息仍然有限。在本研究中,使用机器学习(ANN和ANFIS)和响应面方法(RSM)进行了实验和数值建模,以评估和优化细菌混凝土强度的演变。因此,使用不同浓度的尿素、细胞浓度、钙、营养素和时间,设计了总共58个含有新细菌的混凝土抗压强度试验。结果表明,由于混凝土表层孔隙百分比的降低,细菌混凝土的抗压强度提高了16%;具体地说,与对照混凝土相比,距离混凝土表面5mm。在相同的情况下,机器学习和RSM模型都表明,尿素、钙、营养物和细菌细胞的最佳范围分别为(18-23 g/L)、(150-350 mM)、(1-3 g/L)和2×107个细胞/mL。基于统计分析,ANN和ANFIS模型的RMSE、R2、MPE、RAE和RRSE分别为(0.793、0.785)、(0.985、0.986)、(1.508、1.1)、(0.11、0.09)和(0.121、0.12);从RSM模型中分别获得以下值(0.839、0.972、1.678、0.131和0.165)。因此,可以得出结论,获得了高相关性和最小误差,然而,与RSM模型相比,机器学习模型提供了更准确的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In the present study, both experimentally and numerical modelling using machine learning (ANN and ANFIS) and response surface methodology (RSM) were implemented to evaluate and optimse the evolution of bacterial concrete strength. Therefore, a total of 58 compressive strength tests of the concrete incorporating new bacterial species were designed using different concentrations of urea, cells concentration, calcium, nutrient and time. Based on the results, the compressive strength of the bacterial concrete improved by 16% due to the decrement of the pore percentage in the concrete skin; specifically, 5 mm from the concrete surface, compared to that of the control concrete. In the same context, both machine the learning and RSM models indicated that the optimal range of urea, calcium, nutrient and bacterial cells were (18-23 g/L), (150-350 mM), (1-3 g/L) and 2×107 cells/mL, respectively. Based on the statistical analysis, RMSE, R2, MPE, RAE and RRSE were (0.793, 0.785), (0.985, 0.986), (1.508, 1.1), (0.11, 0.09) and (0.121, 0.12) from both the ANN and ANFIS models, respectively, while; the following values (0.839, 0.972, 1.678, 0.131 and 0.165) was obtained from RSM model, respectively. As such, it can be concluded that a high correlation and minimum error were obtained, however, machine learning models provided more accurate results compared to that of the RSM model.
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来源期刊
Smart Structures and Systems
Smart Structures and Systems 工程技术-工程:机械
CiteScore
6.50
自引率
8.60%
发文量
0
审稿时长
9 months
期刊介绍: An International Journal of Mechatronics, Sensors, Monitoring, Control, Diagnosis, and Management airns at providing a major publication channel for researchers in the general area of smart structures and systems. Typical subjects considered by the journal include: Sensors/Actuators(Materials/devices/ informatics/networking) Structural Health Monitoring and Control Diagnosis/Prognosis Life Cycle Engineering(planning/design/ maintenance/renewal) and related areas.
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