Chao Zhiming, Yang Chuanxin, Zhang Wenbing, Zhang Ye, Zhou Jiaxin
College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 200135, China.
Institute of Water Sciences and Technology, Hohai University, Nanjing 211106, China.
Materials (Basel). 2023 Jul 29;16(15):5330. doi: 10.3390/ma16155330.
The presence of internal fissures holds immense sway over the gas permeability of sustainable cement mortar, which in turn dictates the longevity and steadfastness of associated edifices. Nevertheless, predicting the gas permeability of sustainable cement mortar that contains internal cracks poses a significant challenge due to the presence of numerous influential variables and intricate interdependent mechanisms. To solve the deficiency, this research establishes an innovative machine learning algorithm via the integration of the Mind Evolutionary Algorithm (MEA) with the Adaptive Boosting Algorithm-Back Propagation Artificial Neural Network (ABA-BPANN) ensemble algorithm to predict the gas permeability of sustainable cement mortar that contains internal cracks, based on the results of 1452 gas permeability tests. Firstly, the present study employs the MEA-tuned ABA-BPANN model as the primary tool for gas permeability prediction in cement mortar, a comparative analysis is conducted with conventional machine learning models such as Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) optimised ABA-BPANN, MEA optimised Extreme Learning Machine (ELM), and BPANN. The efficacy of the MEA-tuned ABA-BPANN model is verified, thereby demonstrating its proficiency. In addition, the sensitivity analysis conducted with the aid of the innovative model has revealed that the gas permeability of durable cement mortar incorporating internal cracks is more profoundly affected by the dimensions and quantities of such cracks than by the stress conditions to which the mortar is subjected. Thirdly, puts forth a novel machine-learning model, which enables the establishment of an analytical formula for the precise prediction of gas permeability. This formula can be employed by individuals who lack familiarity with machine learning skills. The proposed model, namely the MEA-optimised ABA-BPANN algorithm, exhibits significant potential in accurately estimating the gas permeability of sustainable cement mortar that contains internal cracks in varying stress environments. The study highlights the algorithm's ability to offer essential insights for designing related structures.
内部裂缝的存在对可持续水泥砂浆的气体渗透性有着巨大影响，进而决定了相关建筑物的寿命和稳定性。然而，由于存在众多影响变量和复杂的相互依存机制，预测含有内部裂缝的可持续水泥砂浆的气体渗透性面临重大挑战。为解决这一不足，本研究通过将思维进化算法（MEA）与自适应增强算法 - 反向传播人工神经网络（ABA - BPANN）集成算法相结合，建立了一种创新的机器学习算法，以基于1452次气体渗透性测试结果预测含有内部裂缝的可持续水泥砂浆的气体渗透性。首先，本研究采用MEA优化的ABA - BPANN模型作为水泥砂浆气体渗透性预测的主要工具，与传统机器学习模型进行比较分析，如粒子群优化算法（PSO）和遗传算法（GA）优化的ABA - BPANN、MEA优化的极限学习机（ELM）以及BPANN。验证了MEA优化的ABA - BPANN模型的有效性，从而证明了其熟练度。此外，借助创新模型进行的敏感性分析表明，含有内部裂缝的耐久性水泥砂浆的气体渗透性受此类裂缝的尺寸和数量影响比受砂浆所承受的应力条件影响更大。第三，提出了一种新颖的机器学习模型，该模型能够建立用于精确预测气体渗透性的解析公式。不熟悉机器学习技术的人员也可使用此公式。所提出的模型，即MEA优化的ABA - BPANN算法，在准确估计不同应力环境下含有内部裂缝的可持续水泥砂浆的气体渗透性方面具有巨大潜力。该研究突出了该算法为设计相关结构提供重要见解的能力。
