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

Abstract

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

CLC Number:

TU528

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.

Figures/Tables 16

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Table 1

Application cases of using machine learning algorithms to predict UHPC mechanical properties"

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

Table 1

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Fig.9

Fig.10

Fig.11

Table 2

Table 3

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References 89

1	Shi C, Wu Z, Xiao J, et al. A review on ultra high performance concrete: part I. raw materials and mixture design[J]. Construction and Building Materials, 2015, 101: 741-751.
2	张超慧. 超高性能混凝土精细化模拟及其力学行为分析[D]. 长沙: 湖南大学土木工程学院, 2021.
	Zhang Chao-hui. Elaborate simulations and analysis of mechanical behavior of ultra-high-performance concrete[D]. Changsha: College of Civil Engineering, Hunan University, 2021.
3	Wang H, Zhang Z, Zhou Z, et al. Experimental and numerical studies on shear behavior of prefabricated bridge deck slabs with compact UHPC wet joint[J]. Case Studies in Construction Materials, 2023, 19: No.e02362.
4	Ashraf M H, Mohamed A, Hassan Y, et al. Effect of nano ferrosilicon and heavyweight fine aggregates on the properties and radiation shielding of ultra-high performance heavyweight concrete[J]. Case Studies in Construction Materials, 2022, 17: No.e01543.
5	Furnas C. Grading aggregates I-mathematical relations for beds of broken solids of maximum density[J]. Journal of Industrial and Engineering Chemistry, 1931, 23(9): 1052-1058.
6	Roy D M, Scheetz B E, Silsbee M R. Processing of optimized cements and concretes via particle packing[J]. Materials Research Bulletin, 1993, 18(3): 45-49.
7	Goltermann P, Johansen V, Palblo L. Packing of aggregates: an alternative tool to determine the optimal aggregate mix[J]. ACI Materials Journal, 1997, 94(5): 435-443.
8	Wong H H C, Kwan A K H. Packing density of cementitious materials: part 1—measurement using a wet packing method[J]. Materials and Structures, 2008, 41(4): 689-701.
9	Kwan A K H, Wong H H C. Packing density of cementitious materials: part 2—packing and flow of OPC+PFA+CSF[J]. Materials and Structures, 2008, 41(4): 773-784.
10	Wang R, Gao X, Huang H, et al. Influence of rheological properties of cement mortar on steel fiber distribution in UHPC[J]. Construction and Building Materials, 2017, 144: 65-73.
11	Mehdipour I, Khayat K H. Effect of particle-size distribution and specific surface area of different binder systems on packing density and flow characteristics of cement paste[J]. Cement and Concrete Composites, 2017, 78: 120-131.
12	Wu Q, An X. Development of a mix design method for SCC based on the rheological characteristics of paste[J]. Construction and Building Materials, 2014, 53: 642-651.
13	Meng W, Valipour M, Khayat K H. Optimization and performance of cost effective ultra-high performance concrete[J]. Materials and Structures, 2017, 50: No.29.
14	Teng L, Meng W, Khayat K H. Rheology control of ultra-high-performance concrete made with different fiber contents[J]. Cement and Concrete Research, 2020, 138: No.106222.
15	Zhou M, Wu Z, Ouyang X, et al. Mixture design methods for ultra-high-performance concrete—a review[J]. Cement and Concrete Composites, 2021, 124: No.104242.
16	Ghafari E, Costa H, Julio E. RSM-based model to predict the performance of self-compacting UHPC reinforced with hybrid steel micro-fibers[J]. Construction and Building Materials, 2014, 66: 375-383.
17	Ghafari E, Costa H, Julio E. Statistical mixture design approach for eco-efficient UHPC[J]. Cement and Concrete Composites, 2015, 55: 17-25.
18	Li Z, Lu D, Gao X. Optimization of mixture proportions by statistical experimental design using response surface method-a review[J]. Journal of Building Engineering, 2021, 36: No.102101.
19	Mosaberpanah M A, Eren O. Statistical models for mechanical properties of UHPC using response surface methodology[J]. Computers and Concrete, 2017, 19(6): 667-675.
20	Ferdosian I, Camoes A. Eco-efficient ultra-high performance concrete development by means of response surface methodology[J]. Cement and Concrete Composites, 2017, 84: 146-156.
21	Roya S, Salehi H, Kodur V, et al. Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams[J]. Engineering Structures, 2020, 224: No.111221.
22	Abuodeh O R, Abdalla J, Hawileh R A. Assessment of compressive strength of ultra-high performance concrete using deep machine learning techniques[J]. Applied Soft Computing, 2020, 95: No.106552.
23	周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.
24	李航. 机器学习方法[M]. 北京: 清华大学出版社, 2022.
25	Wang S, Xia P, Chen K, et al. Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: a review[J]. Journal of Building Engineering, 2023, 80: No.108065.
26	叶永锋. 基于机器学习的注塑制品克重预测及模型可解释性研究[D]. 广州: 华南理工大学机械与汽车工程学院, 2022.
	Ye Yong-feng. Research on gram weight prediction of injection molding products based on machine learning and model interpretability[D]. Guangzhou: School of Mechanical and Automotive Engineering, South China University of Technology, 2022.
27	于玲, 吴铁军. 集成学习: Boosting算法综述[J]. 模式识别与人工智能, 2004, 17(1): 52-59.
	Yu Ling, Wu Tie-jun. Ensemble learning: a review of boosting algorithms[J]. Pattern Recognition and Artificial Intelligence, 2004, 17(1): 52-59.
28	Tran V Q, Dang V Q, Ho L S. Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach[J]. Construction and Building Materials, 2022, 323: No.126578.
29	Finotti R P, Barbosa F D S, Cury A A, et al. Numerical and experimental evaluation of structural changes using sparse auto-encoders and SVM applied to dynamic responses[J]. Applied Sciences, 2021, 11(24): No.11965.
30	Cho H U, Nam Y J, Choi E J, et al. Comparative analysis of the optimized ANN, SVM, and tree ensemble models using Bayesian optimization for predicting GSHP COP[J]. Journal of Building Engineering, 2021, 44: No. 103411.
31	Tran V L, Kim J K. Innovative formulas for reinforcing bar bonding failure stress of tension lap splice using ANN and TLBO[J]. Construction and Building Materials, 2023, 369: No.130500.
32	Zeng Z, Zhu Z, Yao W, et al. Accurate prediction of concrete compressive strength based on explainable features using deep learning[J]. Construction and Building Materials, 2022, 339: No. 127082.
33	Yang G, Wang K C P, Li J Q, et al. Automatic pavement type recognition for image-based pavement condition survey using convolutional neural network[J]. Journal of Computing in Civil Engineering, 2021, 35(1): No.0402006.
34	Han T, Bhat R, Ponduru S A, et al. Deep learning to predict the hydration and performance of fly ash-containing cementitious binders[J]. Cement and Concrete Research, 2023, 165: No.107093.
35	Song H, Ahmad A, Farooq F, et al. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms[J]. Construction and Building Materials, 2021, 308: No. 125021.
36	Ali B, Emadaldin M G. Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves[J]. Journal of Cleaner Production, 2018, 202: 54-64.
37	毛亚娜, 刘世忠, 杏剑, 等.超高性能玻璃砂混凝土-高强钢筋粘结滑移特性及其声发射参数表征[J].吉林大学学报: 工学版, 2023, 53(6): 1686-1694.
	Mao Ya-na, Liu Shi-zhong, Xing Jian, et al. Bond⁃slip characterization between ultra-high performance glass sand concrete and high⁃strength reinforcement based on acoustic emission parameters[J]. Journal of Jilin University (Engineering and Technology Edition), 2023, 53(6): 1686-1694.
38	Sadrossadat E, Basarir H, Karrech A, et al. Multi-objective mixture design and optimisation of steel fiber reinforced UHPC using machine learning algorithms and metaheuristics[J]. Engineering with Computers, 2022, 38(3): 2569-2582.
39	Ehsan G, Mojtaba B, Hugo C, et al. Prediction of fresh and hardened state properties of UHPC: comparative study of statistical mixture design and an artificial neural network model[J]. Journal of Materials in Civil Engineering, 2015, 27(11): No. 4015017.
40	Qian Y F, Sufian M, Accouche O, et al. Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete[J]. Public Library of Science, 2023, 17(12): No.e0278161
41	Mahjoubi S, Meng W N, Yi B. Auto-tune learning framework for prediction of flowability, mechanical properties, and porosity of ultra-high-performance concrete (UHPC)[J]. Applied Soft Computing, 2022, 115: No.108182.
42	Mahjoubi S, Barhemat R, Meng W, et al. AI-guided auto-discovery of low-carbon cost-effective ultra-high performance concrete (UHPC)[J]. Resources Conservation and Recycling, 2023, 189: No.106741.
43	赵筠, 路新瀛. 构建科学智能混凝土配制新技术体系的设想和建议(四)[J]. 混凝土世界, 2020(1): 56-70.
	Zhao Yun, Lu Xin-ying. Ideas and suggestions for building a new technology system for scientific and intelligent concrete preparation(4)[J]. China Concrete, 2020(1): 56-70.
44	Zhang J, Ma G, Huang Y, et al. Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression[J]. Construction and Building Materials, 2019, 210: 713-719.
45	龙武剑, 罗盛禹, 程博远, 等. 机器学习算法用于自密实混凝土性能设计的研究进展[J]. 材料导报, 2024(11): 104-113
	Long Wu-jian, Luo Sheng-yu, Cheng Bo-yuan, et al. Research progress of machine learning algorithm for self-compacting concrete performance design[J]. Materials Reports, 2024(11): 104-113 .
46	Ghafari E, Bandarabadi M, Costa H, et al. Design of UHPC using artificial neural networks[J]. Brittle Matrix Composites, 2012: 61-69.
47	Zhang J, Zhao Y, Li H. Experimental investigation and prediction of compressive strength of ultra-high-performance concrete containing supplementary cementitious[J]. Materials Science and Engineering, 2017, 2017: No.4563164.
48	Qu D, Cai X, Wei C. Evaluating the effects of steel fibers on mechanical properties of ultra-high performance concrete using artificial neural networks[J]. Applied Sciences, 2018, 8(7): No.11202.
49	Joaquin A G. Four-layer perceptron approach for strength prediction of UHPC[J]. Construction and Building Materials, 2020, 256: No.119465.
50	Fan D, Yu R, Fu S, et al. Precise design and characteristics prediction of ultra-high performance concrete (UHPC) based on artificial intelligence techniques[J]. Cement and Concrete Composites, 2021, 122:No. 104171.
51	Joaquin A G. Study of nonlinear relationships between dosage mixture design and the compressive strength of UHPC[J]. Case Studies in Construction Materials, 2022, 17: No.e01228.
52	Joaquin A G, Jaime F G, Nancy T C. Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks[J]. Journal of Environmental and Civil Engineering, 2022, 26(6): 2319-2343.
53	Nguyen N, Abellan-García J, Lee S, et al. Efficient estimating compressive strength of ultra-high performance concrete using XG Boost model[J]. Journal of Building Engineering, 2022, 52: No.104302.
54	Shen Z, Deifalla A F, Kaminski P, et al. Compressive strength evaluation of ultra-high-strength concrete by machine learning[J]. Materials, 2022, 15(10): 3523.
55	Saleh E, Tarawneh A, Naser M Z, et al. You only design once (YODO): Gaussian process-batch Bayesian optimization framework for mixture design of ultra high performance concrete[J]. Construction and Building Materials, 2022, 330: No.127270.
56	Alabduljabbar H, Khan M, Awan H H, et al. Predicting ultra-high-performance concrete compressive strength using gene expression programming method[J]. Case Studies in Construction Materials, 2023, 18: No.e02074.
57	Guo P, Mahjoubi S, Liu K J, et al. Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC)[J]. Case Studies in Construction Materials, 2023, 19: No.e2625.
58	Tavares C, Wang X, Saha S, et al. Machine learning-based mix design tools to minimize carbon footprint and cost of UHPC. part 1: efficient data collection and modeling[J]. Cleaner Materials, 2022, 4: No.100082.
59	Iqbal K M, Yassir M A. Intelligent data-driven compressive strength prediction and optimization of reactive powder concrete using multiple ensemble-based machine learning approach[J]. Construction and Building Materials, 2023, 404: No.133148.
60	Nguyen M H, Nguyen T A, Hai-Bang LY. Ensemble XGBoost schemes for improved compressive strength prediction of UHPC[J]. Structures, 2023, 57: No.105062.
61	Naseri H, Jahanbakhsh H, Hosseini P, et al. Designing sustainable concrete mixture by developing a new machine learning technique[J]. Journal of Cleaner Production, 2020, 258:No. 120578.
62	Erdal H I, Karakurt O, Namli E. High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transforms engineering[J]. Applications of Artificial Intelligence, 2013, 26: 1246-1254.
63	Cai R, Han T, Liao W, et al. Prediction of surface chloride concentration of marine concrete using ensemble machine learning[J]. Cement and Concrete Research, 2020, 136: No.106164.
64	Kang M C, Yoo D Y, Gupta R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete[J]. Construction and Building Materials, 2021, 266: No.121117.
65	Li Q, Song Z. High-performance concrete strength prediction based on ensemble learning[J]. Construction and Building Materials, 2022, 324: No.126694.
66	Wang Q C, Hussain A, Farooqi M U, et al. Artificial intelligence-based estimation of ultra-high-strength concrete's flexural property[J]. Case Studies in Construction Materials, 2022, 17: No.e01243.
67	Das P, Kashem A. Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations[J]. Case Studies in Construction Materials, 2024, 20: No.e2723.
68	Dong W, Huang Y, Cui A, et al. Mix design optimization for fly ash-based geopolymer with mechanical, environmental, and economic objectives using soft computing technology[J]. Journal of Building Engineering, 2023, 72: No.106577.
69	陈权, 李莉, 陈永乐, 等. 面向深度学习可解释性的对抗攻击算法[J]. 计算机应用, 2022, 42(2): 510-518.
	Chen Quan, Li Li, Chen Yong-le, et al. Adversarial attack algorithm for deep learning interpretability[J]. Journal of Computer Applications, 2022, 42(2): 510-518.
70	李扬. 基于随机森林模型解释的越野路面识别算法研究[D]. 长春: 吉林大学交通学院, 2022.
	Li Yang. Research on off-road pavement recognition algorithm based on random forest model interpretation[D]. Changchun: College of Transportation, Jilin University, 2022.
71	Wu Y, Zhou Y. Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete[J]. Construction and Building Materials, 2022, 330: No.127298.
72	Kina C, Turk K, Tanyildizi H. Deep learning and machine learning-based prediction of capillary water absorption of hybrid fiber reinforced self-compacting concrete[J]. Structural Concrete, 2022, 23(5): 3331-3358.
73	Liu K, Zheng J, Zhao X, et al. Innovative modeling framework of chloride resistance of recycled aggregate concrete using ensemble-machine-learning methods[J]. Construction and Building Materials, 2022, 337: No.127613.
74	Biswas R, Li E, Zhang N, et al. Development of hybrid models using metaheuristic optimization techniques to predict the carbonation depth of fly ash concrete[J]. Construction and Building Materials, 2022, 346: No.128483.
75	Liu K, Alams M S, Zhu J, et al. Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms[J]. Construction and Building Materials, 2021, 301:No. 124382.
76	吴贤国, 陈彬, 杨赛, 等. 基于RF-NSGA-Ⅱ算法的高耐久性混凝土配合比优化研究[J]. 工业建筑, 2021, 5(17): 156-161.
	Wu Xian-guo, Chen Bin, Yang Sai, et al. Study on optimization of high durability concrete mix ratio based on RF-NSGA-Ⅱ algorithm[J]. Industrial Construction, 2021, 5(17): 156-161.
77	Selim C, Enes G, Onur O, et al. A novel prediction model for durability properties of concrete modified with steel fiber and silica fume by using Hybridized GRELM[J]. Construction and Building Materials, 2022, 341: No.127856.
78	Sahoo S, Mahapatra T R. ANN modeling to study strength loss of fly ash concrete against long term sulphate attack[J]. Materials Today: Proceedings, 2018, 5(11): 24595-24604.
79	Liu K, Dai Z, Zhang R, et al. Prediction of the sulfate resistance for recycled aggregate concrete based on ensemble learning algorithms[J]. Construction and Building Materials, 2022, 317: No.125917.
80	Wang S, Peng X, Chen K, et al. Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: a review[J]. Journal of Building Engineering, 2023, 80: No.108065.
81	曹鑫. 超高性能混凝土的制备及其性能研究[D]. 合肥: 安徽建筑大学土木工程学院, 2022.
	Cao Xin. Study on preparation and properties of ultra-high-performance concrete[D]. Hefei: College of Civil Engineering, Anhui Jian Zhu University, 2022.
82	刘艳彪, 乔建质, 尤世界. 机器学习在碳基环境功能材料领域的应用研究进展[J]. 环境工程, 2022, 40(6): 182-187.
	Liu Yan-biao, Qiao Jian-zhi, You Shi-jie. Research progress on application of machine learning in the field of carbon-based environmental functional materials[J]. Environmental Engineering, 2022, 40(6): 182-187.
83	Tipu R K, Panchal V R, Pandya K S. Multi-objective optimized high-strength concrete mix design using a hybrid machine learning and metaheuristic algorithm[J]. Asian Journal of Civil Engineering, 2023, 24: 849-867.
84	Fann D, Zhu J, Fan M, et al. Intelligent design and manufacturing of ultra-high performance concrete (UHPC)-a review[J]. Construction and Building Materials, 2023, 385:No. 131495.
85	Sun G, Du M, Shan B, et al. Ultra-high performance concrete design method based on machine learning model and steel slag powder[J]. Case Studies in Construction Materials, 2022, 17: No.e1682.
86	Dua D, Graff C. UCI Machine learning repository[EB/OL]. [2024-01-12].
87	Jain N, Shyue P O, Hautier G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation[J]. APL Materials, 2013, 1(1): No.11002.
88	Young B A, Hall A, Pilon L, et al. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods[J]. Cement and Concrete Research, 2019, 115: 379-388.
89	DeRousseau M A, Laftchiev J R E., Kasprzyk, et al. A comparison of machine learning methods for predicting the compressive strength of field-placed concrete[J]. Construction and Building Materials, 2019, 228: No.116661.

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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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Research progress on application of artificial intelligence in ultra⁃high performance concrete

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