State Key Laboratory of Precision Blasting, Jianghan University, Wuhan, 430056, China; Hubei Key Laboratory of Blasting Engineering, Jianghan University, Wuhan, 430056, China.
State Key Laboratory of Precision Blasting, Jianghan University, Wuhan, 430056, China; Hubei Key Laboratory of Blasting Engineering, Jianghan University, Wuhan, 430056, China; School of Resources and Safety Engineering, Central South University, Changsha, Hunan, 410083, China.
J Environ Manage. 2024 Dec;371:123068. doi: 10.1016/j.jenvman.2024.123068. Epub 2024 Oct 30.
This study proposed a data driven approach to predict the compressive strength (CS) of recycled aggregate concrete (RAC) for sustainable construction using an elite single genetic optimization algorithm-based cascade forward neural network (ESGA-CFNN) model. It was applied to 272 RAC samples under different conditions and compositions focusing on key parameters for CS prediction: water-to-cement ratio (WCR), water absorption (WA), recycled coarse aggregate (RCA) density, fine aggregate (FA) density, naturally occurring coarse aggregate (NCA) density and water-to-total material ratio (WTMR). These parameters were used to develop the ESGA-CFNN model which was then evaluated for its performance. To compare the ESGA-CFNN model, two other models were developed and compared: particle swarm optimization-based CFNN (PSO-CFNN) and artificial bee colony-based CFNN (ABC-CFNN). K-fold cross-validation was used during model development to prevent overfitting. Results showed that ESGA-CFNN model performed better with an RMSE (root-mean-squared error) of 1.144, R (determination coefficient) of 0.991 and a-index of 1.000. ABC-CFNN model had an RMSE of 1.434, R of 0.987 and a-index of 0.982 while PSO-CFNN had an RMSE of 1.561, R of 0.984 and a-index of 0.982. Practical validation with 6 RAC samples confirmed the real world applicability of these models. The findings of this study showed that the proposed ESGA-CFNN model is important for quality control in RAC production and optimizing mix designs to achieve required compressive strength to meet standards and reduce cost and increase sustainability in concrete construction. This study introduces a novel hybrid approach combining ESGA-CFNN, PSO-CFNN, and ABC-CFNN algorithms for accurately predicting the compressive strength of RAC. These models outperform traditional methodologies by offering enhanced predictive accuracy and generalization capability, especially in complex, real-world datasets.
本研究提出了一种基于精英单遗传优化算法的级联前馈神经网络（ESGA-CFNN）模型的数据驱动方法，用于可持续建筑中预测再生骨料混凝土（RAC）的抗压强度（CS）。该模型应用于 272 个不同条件和组成的 RAC 样本，重点关注 CS 预测的关键参数：水灰比（WCR）、吸水率（WA）、再生粗骨料（RCA）密度、细骨料（FA）密度、天然粗骨料（NCA）密度和水与总材料比（WTMR）。这些参数用于开发 ESGA-CFNN 模型，然后对其性能进行评估。为了比较 ESGA-CFNN 模型，还开发并比较了另外两个模型：基于粒子群优化的 CFNN（PSO-CFNN）和基于人工蜂群的 CFNN（ABC-CFNN）。在模型开发过程中使用 K 折交叉验证来防止过拟合。结果表明，ESGA-CFNN 模型的 RMSE（均方根误差）为 1.144、R（确定系数）为 0.991 和 a 指数为 1.000，表现更好。ABC-CFNN 模型的 RMSE 为 1.434、R 为 0.987 和 a 指数为 0.982，PSO-CFNN 模型的 RMSE 为 1.561、R 为 0.984 和 a 指数为 0.982。用 6 个 RAC 样本进行实际验证证实了这些模型在现实世界中的适用性。本研究的结果表明，所提出的 ESGA-CFNN 模型对于 RAC 生产中的质量控制以及优化配合比设计以达到所需的抗压强度以满足标准、降低成本和提高混凝土施工的可持续性非常重要。本研究介绍了一种新的混合方法，结合了 ESGA-CFNN、PSO-CFNN 和 ABC-CFNN 算法，用于准确预测 RAC 的抗压强度。这些模型通过提供增强的预测准确性和泛化能力，特别是在复杂的真实数据集上，优于传统方法。
