硅酸盐学报

基于可解释动态集成学习的泵管内混凝土输运非稳态智能评估方法

PDF下载

赵国欣 ¹^,² , 唐昀超 ³ , 万帅 ⁴ , 陈犇 ⁵ , 陈正 ¹^,²

硅酸盐学报 | 研究论文 2026,54(8): 2602-2613

收起

硅酸盐学报 |研究论文 2026 , 54 (8) : 2602 -2613

基于可解释动态集成学习的泵管内混凝土输运非稳态智能评估方法

全屏

赵国欣¹^,², 唐昀超³, 万帅⁴, 陈犇⁵, 陈正¹^,²

作者信息

^1．广西大学土木建筑工程学院，南宁 530004

^2．广西大学广西防灾减灾与工程安全重点实验室，南宁 530004

^3．广东省城市生命线工程智能防灾与应急技术重点实验室，东莞理工学院，广东东莞 523000

^4．新疆大学机械工程学院，乌鲁木齐 830049

^5．中国电建集团华东勘测设计研究院有限公司，杭州 311100

通讯作者:

陈正（1982—），男，博士，教授。

作者简介:

赵国欣（1999—），男，博士研究生。

Intelligent Evaluation Method for Non-Steady Transport State of Concrete in Pump Pipe Based on Interpretable Dynamic Ensemble Learning

Guoxin ZHAO¹^,², Yunchao TANG³, Shuai WAN⁴, Ben CHEN⁵, Zheng CHEN¹^,²

Affiliations

^1.School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China

^2.Guangxi Key Laboratory of Disaster Prevention and Structural Safety, Guangxi University, Nanning 530004, China

^3.Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for Urban Lifeline Engineering, Dongguan University of Technology, Dongguan 523000, Guangdong, China

^4.Intelligent Manufacturing Modern Industry College, Xinjiang University, Urumqi 830049, China

^5.Powerchina Huadong Engineering Corporation Limited, Hangzhou 311100, China

Corresponding Author:

{CHEN} Zheng (1982-), male, Ph.D, Professor. E-mail: chenzheng@gxu.edu.cn

ZHAO Guoxin (1999-), male, Doctoral candidate. E-mail: guoxinzhao@st.gxu.edu.cn

出版时间: 2026-07-10 doi: 10.14062/j.issn.0454-5648.20260104

文章导航

摘要

收起

针对长距离混凝土泵送过程中流变非稳态特征显著、极易诱发堵管灾变，且传统物理监测预警滞后、工程现场故障样本严重稀缺等行业痛点，本工作提出了一种基于可解释动态集成学习的泵管内混凝土输运非稳态智能评估新方法。依托大跨径钢管混凝土拱桥灌注工程，自主研发高频压力传感系统以获取多维泵压时程序列；深度融合流变学物理先验知识，提取了斜率、标准差及极差等表征流变阻力演化的时域统计特征参量。针对故障工况导致的极端类别不平衡难题，引入合成少数类过采样技术重构特征空间数据分布，有效破除模型训练偏倚。在此基础上，构建基于贝叶斯优化的动态自适应集成学习架构对泵送演化态进行高精度感知。实验结果表明：所提动态自适应集成模型有效克服了非平衡数据陷阱，在测试集上的总体预测准确率达0.92。特别是对“压力累积”与“堵管”两类极危演化态的识别准确率分别提升至91.43%和88.00%，显著优于单一基模型及传统投票策略。此外，引入沙普利加性解释方法深度解构模型决策黑箱，证实泵压变化曲线斜率为判定堵管状态的核心主导特征，其演化规律与管内摩阻及流变阻力机制高度吻合，赋予了数据驱动模型清晰的物理合理性。本工作成功验证了基于时序特征非侵入式监测的可行性，有效破解了小样本故障工况下的漏报难题，为功能型水泥基材料的智能化施工、早期预警调控及人工智能落地应用提供了可靠的理论与技术支撑。

关键词

混凝土泵送 / 时序特征空间 / 动态集成学习 / 可解释人工智能 / 非平衡数据重构 / 输运非稳态

Abstract

收起

Introduction

Concrete pumping technology is widely applied in major construction projects. However, under long-distance or complex conditions, concrete transport often exhibits strongly non-steady characteristics due to the coupled factors like material rheology and pipeline topologies. This instability easily induces abnormal resistance surges, evolving into severe blockages. Conventional monitoring relies on post-event management or static pressure thresholds, triggering alarms only when pressure breaches a safety limit. Essentially, concrete blockage is a progressive dynamic accumulation of rheological resistance, leaving a critical "pressure accumulation" intermediate state. Static thresholds ignore this, causing severe pre-warning lag. AI methods show a potential, while intelligent perception faces three challenges, i.e., the binary classification defect overlooking the transitional state, the "majority class trap" from extreme class imbalance, and the contradiction between prediction accuracy and physical interpretability. To address these bottlenecks, this study was to propose a novel intelligent evaluation framework for non-steady concrete transport, deeply integrating rheological mechanisms with advanced machine learning architectures.

Method

The research relied on the Zhenglong Hongshui River Bridge project, China. An embedded monitoring system with 600 Hz dynamic pressure sensors was seamlessly welded to the straight pipeline's inner wall near the pump outlet to acquire high-quality rheological resistance signals. During feature engineering, mechanical noises from valve reversing were eliminated, retaining only effective pressure-holding signals during main cylinder propulsion. Since deriving the exact friction coefficient via true flow velocity is a challenge in field engineering, macroscopic proxy variables were utilized. Assuming constant propulsion speed, the slope of the pressure curve was extracted to represent the transient growth rate of rheological friction. Standard deviation and range were extracted to quantify slip-layer stability and flow turbulence. To address extreme class imbalance in the 691-sample dataset (78.3% normal), the Synthetic Minority Over-sampling Technique (SMOTE) was applied to reconstruct the feature space, eliminating training bias. Subsequently, four ensemble algorithms (i.e., RF, XGBoost, LightGBM, GBDT) optimized by the Tree-structured Parzen Estimator (TPE) were used as base learners. Finally, a dynamic adaptive ensemble fusion architecture calculated sample-specific dynamic weights based on prediction confidence, replacing conventional static voting mechanisms.

Results and Discussion

The extracted time-domain features effectively map the dynamic evolution of in-tube rheological resistance. After SMOTE reconstruction and TPE optimization, the base models' potential to capture minority class abnormal conditions is maximized. During the comparative evaluation of the ensemble models, the study addresses the "accuracy paradox" inherent in highly imbalanced datasets. Conventional soft voting and hard voting models achieve an overall accuracy of approximately 0.90 simply because of the overwhelming majority class, dangerously masking high misdiagnosis rates for critical anomalies. The overall accuracy improves to 0.92 via implementing the dynamic adaptive fusion strategy. More importantly, the model substantially suppresses the omission rates for high-risk conditions. The recall rate for the "pressure accumulation" state reaches 91.43%, representing a 10.3% relative improvement over the soft voting method (i.e., 82.86%). For the most hazardous "blockage" state, the recall reaches 88.00%, outperforming the soft voting method (i.e., 76.00%) with a remarkable relative improvement of 15.8%. Furthermore, a SHapley Additive exPlanations (SHAP) visualization framework is introduced to verify the model's decision logic globally and locally. The global feature importance ranking confirms that the pressure curve slope is an absolute dominant feature triggering blockage predictions, followed by range and standard deviation. The SHAP beeswarm summary plots reveal that a sharp increase in these three features heavily biases the model toward a "blockage" decision. Conversely, smaller feature values drive the model toward a "normal" prediction. Local waterfall plots for individual samples further detail the positive and negative contributions of each feature to a specific prediction. This data-driven logic perfectly coincides with the physical reality that pipeline blockages instantly cause a sharp surge in rheological friction and severe dispersion in pressure values due to slip layer disruption. SHAP effectively explains the statistical feature attribution, and its strong alignment with engineering experience proves the model's physical rationality. Verifying strict causality requires a further extensive field validation across multiple complex scenarios.

Conclusions

Time-domain physical features validated the ability to characterize in-tube non-steady transport. The slope, standard deviation, and range extracted from high-frequency pressure sequences could serve as effective macroscopic proxies for concrete fluid resistance and slip-layer stability. The combination of SMOTE feature space reconstruction and TPE Bayesian hyperparameter tuning effectively overcome the small-sample fault warning dilemma under extreme class imbalance, eliminating training bias toward majority classes. The dynamic adaptive ensemble architecture demonstrated a core advantage in suppressing high-risk omission rates. It leveraged a minor overall precision increment to drastically reduce crucial fault omission risks via tolerating a minimal sacrifice in majority class accuracy, delivering an ample forward-intervention window. The SHAP attribution analysis achieved a white-box deconstruction of the ensemble decision logic. The alignment between the algorithm's reliance on slope surged and the true physical laws of rheological friction endowed the model with clear physical rationality, laying a solid theoretical foundation for the trustworthy application of AI technologies in intelligent civil engineering construction.

Key words

concrete pumping / time-series feature space / dynamic ensemble learning / interpretable artificial intelligence / imbalanced data reconstruction / non-steady transport state

引用本文

赵国欣, 唐昀超, 万帅, 陈犇, 陈正. 基于可解释动态集成学习的泵管内混凝土输运非稳态智能评估方法. 硅酸盐学报, 2026 , 54 (8) : 2602 -2613 . DOI: 10.14062/j.issn.0454-5648.20260104

Guoxin ZHAO, Yunchao TANG, Shuai WAN, Ben CHEN, Zheng CHEN. Intelligent Evaluation Method for Non-Steady Transport State of Concrete in Pump Pipe Based on Interpretable Dynamic Ensemble Learning[J]. Journal of the Chinese Ceramic Society, 2026 , 54 (8) : 2602 -2613 . DOI: 10.14062/j.issn.0454-5648.20260104

基金

收起

国家自然科学基金面上项目(52478242)
广西科技重大专项(桂科AA23073017)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

2026年第54卷第8期

PDF下载

引用本文

BibTeX

文章信息

doi: 10.14062/j.issn.0454-5648.20260104

接收时间：2026-02-28
首发时间：2026-10-22
出版时间：2026-07-10

补充材料

相关文章

文章信息

作者

出版历史

收稿日期：2026-02-28
修回日期：2026-03-14

基金

国家自然科学基金面上项目(52478242)

广西科技重大专项(桂科AA23073017)

作者信息

^1．广西大学土木建筑工程学院，南宁 530004

^2．广西大学广西防灾减灾与工程安全重点实验室，南宁 530004

^3．广东省城市生命线工程智能防灾与应急技术重点实验室，东莞理工学院，广东东莞 523000

^4．新疆大学机械工程学院，乌鲁木齐 830049

^5．中国电建集团华东勘测设计研究院有限公司，杭州 311100

通讯作者:

陈正（1982—），男，博士，教授。

参考文献

分享链接

https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20260104

分享至

全文二维码

扫描看全文

引用本文

BibTeX

本文的引用情况

2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

关闭全屏

BibTeX
EndNote
RefWorks
TxT