人工智能在超高性能混凝土中的应用研究进展

doi:10.13229/j.cnki.jdxbgxb.20240056

摘要/Abstract

摘要：

传统的超高性能混凝土配合比设计存在成本高、效率低、整体过程复杂等问题。人工智能技术的应用可以相对快速度且准确地预测超高性能混凝土的各项性能，实现超高性能混凝土配合比设计的智能化和绿色化。通过梳理人工智能技术在超高性能混凝土性能预测和配合比设计方面的研究进展，剖析了该领域目前主流技术尚存在的一系列问题，包括数据质量、模型验证、模型可解释性、多目标优化等问题。结合人工智能技术和学科理论，提出了针对相关问题的解决方案。

关键词: 土木工程材料, 超高性能混凝土, 人工智能, 配合比设计, 特征工程, 多目标优化

Abstract:

There are some disadvantages in the traditional methodology of the mix proportion design of ultra-high performance concrete， such as high cost， low efficiency， and complex process. The application of artificial intelligence technology can help to overcome these shortcomings and intelligently predict various properties， thus intelligent and sustainable mix proportion designs can be delivered. By the review on the research progress of artificial intelligence technology applied in the properties prediction and mix proportion design of ultra-high-performance concrete， obstacles and challenges in current mainstream technologies were pointed out， including data quality， model validation， model interpretability， and multi-objective optimization. In order to resolve these issues， many specific proposals were put forward based on the combination of artificial intelligence technology and discipline theory of building materials.

Key words: civil engineering materials, ultra-high performance concrete, artificial intelligence, mix proportion design, feature engineering, multi-objective optimization

中图分类号:

TU528

袁杰,王军博,陈歆,黄馨,张傲翔,崔安琪. 人工智能在超高性能混凝土中的应用研究进展[J]. 吉林大学学报(工学版), 2025, 55(3): 771-789.

Jie YUAN,Jun-bo WANG,Xin CHEN,Xin HUANG,Ao-xiang ZHANG,An-qi CUI. Research progress on application of artificial intelligence in ultra⁃high performance concrete[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 771-789.

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机器学习算法在UHPC力学性能上的应用"

参考文献	发表时间	模型	数据/组	输入变量	输出变量	评价指标R²
［46］	2012	BPNN	53	水泥、硅灰、水、石英粉、砂、钢纤维掺量、减水剂	抗压强度	0.96~0.99
［47］	2017	BPNN	78	水泥、粉煤灰、硅灰、水、粗细骨料、减水剂、水胶比、砂胶比、砂率、骨料粒径范围	抗压强度	0.99
［48］	2018	BPNN	162	水胶比、钢纤维掺量、钢纤维长度、钢纤维直径、钢纤维长径比	抗压强度	0.99
［48］	2018	BPNN	102	水胶比、钢纤维掺量、钢纤维长度、钢纤维直径、钢纤维长径比	抗折强度	0.99
［49］	2020	ANN	927	水泥、粉煤灰、粒化高炉矿渣粉、硅灰、再生玻璃粉、稻壳灰、FC3R（流化催化裂化残渣）、偏高岭土、石灰石粉、水、石英粉、减水剂	抗压强度	0.86
［50］	2021	GA-ANN	80	水泥、硅灰、石灰石粉、水、砂、水胶比	抗压强度	0.95~0.99
［51］	2022	RFM	931	水泥、粉煤灰、硅灰、粒化高炉矿渣粉、再生玻璃粉、稻壳灰、FC3R（流化催化裂化残渣）、偏高岭土、石灰石粉、水、石英粉、集料、减水剂、水胶比、集料最大粒径、虚拟包装密度	抗压强度	0.90
［52］	2022	BPNN	265	水泥、硅灰、再生玻璃粉、FC3R（流化催化裂化残渣）、石灰石粉、水、石英粉、砂、减水剂、水胶比、水灰比、虚拟包装密度、龄期	抗压强度	0.97~0.98
［53］	2022	XG Boost	931	水泥、粉煤灰、粒化高炉矿渣粉、硅灰、再生玻璃粉、稻壳灰、FC3R（流化催化裂化残渣）、偏高岭土、石灰石粉、水、集料、石英粉、减水剂、集料最大粒径、水灰比、水胶比、虚拟包装密度	抗压强度	0.89~0.99
［54］	2022	XG Boost\AdaBoost	372	水泥、粉煤灰、硅灰、水、砂、减水剂、钢纤维体积分数	抗压强度	0.90
［55］	2022	GP-BBO	110	水泥、粉煤灰、硅灰、水、石英粉、砂、钢纤维、减水剂	抗压强度	0.90
［56］	2023	GEP	810	水泥、粉煤灰、粒化高炉矿渣粉、硅灰、纳米二氧化硅、石灰石粉、水、粗细集料、石英粉、纤维、高效减水剂、温度、相对湿度、龄期	抗压强度	0.997
［57］	2023	Automated machine learning	968	水泥、粉煤灰、粒化高炉矿渣粉、硅灰、纳米二氧化硅、石灰石粉、水、石英粉、石英砂、直钢纤维、钩状钢纤维、聚丙烯纤维、高效减水剂等	-	0.95

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模型	优点	缺点	应用情况
神经网络	①采用BP神经网络进行性能预测，预测精度高；②数据库数据较复杂时表现良好，神经网络模型可以充分逼近任意复杂的非线性关系；③求解能力强，可以在复杂情况下快速得到最优解	①需要较大的数据库来支撑，否则泛化能力较差；②算法的超参数较多；调整超参数需要较强的理论支撑；③黑盒模型即可解释性差	★★★★
集成学习	①RF 能处理UHPC的非线性高维度数据样本，不需降维；②不需要数据归一化；③可以判断特征的重要性；④异质融合模型可以进一步提高模型的预测能力及泛化能力	①RF在存在较多异常值的数据集上会过拟合；②不能较好地处理非平衡数据的问题；③GBDT的基学习器之间存在依赖关系、难以并行训练数据	★★★
SVM	①在数据集较小的情况下表现良好；②数学原理清晰，可解释性强；③泛化能力强	①随着样本复杂度提高，模型的时间复杂度呈指数型上升，不适用于数据集较大的情况；②核函数调整较复杂且需要理论基础支持；③在样本精度要求高、存在异常值的情况下表现较差	★★
ML+优化算法	①ML模型调参复杂，利用智能优化算法可快速得到合适的参数；②混合使用较为方便	①容易陷入局部最优而非全局最优；②针对离散的问题表现较差	★★★
ML+多目标优化	①在求解复杂的多目标问题时表现良好；②应用于UHPC配合比设计时可根据使用者需求得到相应的帕累托解集	同时优化的目标函数过多时模型的空间复杂度和时间复杂度会大幅度增加	★★★

数据预处理		可解释性
特征工程	①方差过滤；②F检验；③互信息法；④嵌入法；⑤包装法；⑥主成分分析（PCA）；⑦核主成分分析（KPCA）	可解释性模型	①线性逻辑回归模型；②决策树模型；③聚类模型；④朴素贝叶斯模型
异常值处理	①隔离森林；②聚类方法；③箱型图法	独立于模型的解释方法	①部分依赖图（PDP）；②个体条件期望ICE；③Shapley Value；④神经解释图（NID）

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