Advanced Engineering Informatics最新文献_第10页

Advancing quality control in off-site construction with large language models enhanced by hybrid retrieval-augmented generation 利用混合检索增强生成增强的大型语言模型推进非现场施工的质量控制

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-02-07 DOI: 10.1016/j.aei.2026.104381

Fanfan Meng, Mi Pan

Quality control (QC) is critical for off-site construction (OSC), but it still relies heavily on the knowledge and expertise of inspectors. Projects face challenges involving heterogeneous and fragmented knowledge from multiple stakeholders across different stages, compounded by skilled labor shortage, subjective biases, and human errors. A consistent and reliable approach is needed to guide knowledge-informed QC in OSC, yet currently lacking. This paper aims to develop a novel knowledge-driven framework for advancing off-site construction quality control, empowered by hybrid retrieval-augmented generation (hybrid RAG)-enhanced large language models (LLMs). The hybrid RAG employs a prompt-based approach for entity and relationship extraction to support automated graph construction from unstructured knowledge. Then, a semantic alignment approach is designed to align dense retrieval, sparse retrieval, and subgraph traversal for vector-graph hybrid retrieval, thereby enabling the LLMs to generate more reliable outputs for complex QC decision-making scenarios. Comparative analysis against baseline RAG was conducted on three designed use cases, containing quality information retrieval, quality compliance checking, and quality control task guidance, using three broadly used open-source LLMs, namely DeepSeek-R1-14B, GPT-OSS-20B, and Qwen3-14B. The results demonstrate the superiority of the proposed hybrid RAG in significantly improving model response accuracy, trustworthiness and reliability. This study further demonstrates that medium-sized LLMs can effectively address complex retrieval and generation tasks guided by appropriate approaches. The findings of this study offer valuable insights for advancing construction QC practices, and inform future research in consistent and reliable knowledge retrieval for addressing knowledge-intensive tasks in the architecture, engineering and construction industry.

质量控制（QC）对非现场施工（OSC）至关重要，但它仍然严重依赖于检查员的知识和专业知识。项目面临的挑战包括来自不同阶段的多个涉众的异构和碎片化的知识，以及熟练劳动力短缺、主观偏见和人为错误。在OSC中，需要一个一致和可靠的方法来指导知识灵通的QC，但目前缺乏。本文旨在通过混合检索-增强生成(hybrid RAG)-增强的大型语言模型（llm），开发一种新的知识驱动框架，用于推进非现场施工质量控制。混合RAG采用基于提示的方法进行实体和关系提取，以支持从非结构化知识中自动构建图形。然后，设计了一种语义对齐方法，对向量图混合检索中的密集检索、稀疏检索和子图遍历进行对齐，从而使llm能够为复杂的QC决策场景生成更可靠的输出。采用DeepSeek-R1-14B、GPT-OSS-20B和Qwen3-14B三种广泛使用的开源llm，对包含质量信息检索、质量符合性检查和质量控制任务指导的三个设计用例与基线RAG进行对比分析。结果表明，该算法显著提高了模型响应精度、可信度和可靠性。本研究进一步证明，在适当的方法指导下，中型llm可以有效地解决复杂的检索和生成任务。本研究的发现为推进建筑质量控制实践提供了有价值的见解，并为未来的研究提供了一致和可靠的知识检索，以解决建筑、工程和建筑行业的知识密集型任务。

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引用次数: 0

Advancing sustainable agriculture in Atlantic Canada through HYDRA-SE: A hybrid decision and regression stacked ensemble for yield prediction 通过HYDRA-SE推进加拿大大西洋地区的可持续农业：用于产量预测的混合决策和回归堆叠集合

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-02-11 DOI: 10.1016/j.aei.2026.104433

Avneet Kaur , Raheleh Malekian , Gurjit S. Randhawa , Aitazaz A. Farooque , Ebrahim Hashemi Garmdareh , Bishnu Acharya , Rajandeep Singh , Gurpreet S. Selopal

Crop yield prediction is a fundamental component of global food security and sustainable agricultural planning. Potato, a key staple and cash crop in Atlantic Canada, presents challenges for yield prediction due to the complex nature of the relationships between soil parameters and crop production. In this research, we developed a machine learning (ML) framework that integrates multi-method feature selection and stacked ensemble learning to predict potato yield. The datasets consisted of soil analytical, geospatial, and productivity parameters from potato fields across Prince Edward Island (PEI) and New Brunswick (NB) during the 2017–2018 growing seasons. Stepwise regression-based feature selection identified eight significant features from 33 initial features that were included for training 16 baseline ML algorithms. The five top-performing models were used to further develop the proposed ensemble and stacked ensemble learning methods. Overall, the results indicated that the CatBoost, XGBoost, LightGBM, random forest (RF), and bagged trees outperformed other traditional ML methods. The proposed hybrid yield decision and regression approach- stacked ensemble (HYDRA-SE) framework achieved the highest accuracy (R² = 0.88 and RMSE = 3.75). These results illustrate the significant potential of advanced ensemble learning methods to produce reliable and robust predictive capacity for potato yield prediction at a field level, regional level, and perhaps at the national level and can provide a decision-support system for potato precision agriculture (PA) in Atlantic Canada.

作物产量预测是全球粮食安全和可持续农业规划的基本组成部分。马铃薯是加拿大大西洋地区的主要主食和经济作物，由于土壤参数与作物产量之间关系的复杂性，马铃薯对产量预测提出了挑战。在这项研究中，我们开发了一个机器学习（ML）框架，该框架集成了多方法特征选择和堆叠集成学习来预测马铃薯产量。这些数据集包括2017-2018年生长季节爱德华王子岛（PEI）和新不伦瑞克省（NB）马铃薯田的土壤分析、地理空间和生产力参数。基于逐步回归的特征选择从33个初始特征中识别出8个重要特征，这些特征包括用于训练16个基线ML算法。五个表现最好的模型被用来进一步发展所提出的集成和堆叠集成学习方法。总体而言，结果表明CatBoost、XGBoost、LightGBM、随机森林（RF）和袋装树优于其他传统的机器学习方法。所提出的混合产量决策与回归方法-叠置集成（HYDRA-SE）框架获得了最高的精度（R2 = 0.88, RMSE = 3.75）。这些结果说明了先进的集成学习方法在田间、区域甚至国家层面上产生可靠和稳健的马铃薯产量预测能力的巨大潜力，并可以为加拿大大西洋地区的马铃薯精准农业（PA）提供决策支持系统。

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引用次数: 0

Generative AI-driven data augmentation and object-guided vision-language reasoning for PPE compliance analysis in work-at-height 高空作业PPE符合性分析的生成人工智能驱动数据增强和对象引导视觉语言推理

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-24 DOI: 10.1016/j.aei.2026.104364

Wenyu Xu , Wen Yi , Yi Tan

PPE compliance is a fundamental prerequisite for ensuring safety in work-at-height. Although computer vision has advanced PPE detection, challenges remain in dataset scarcity that limits generalization and in weak semantic reasoning that hinders reliable compliance verification. To address these limitations, this paper presents a generative AI-driven data augmentation and an object-guided vision-language model (VLM) to analyze PPE compliance in work-at-height. Safety standards on work-at-height and PPE (e.g., GB 80-2016, GB 2811-2019) are formalized via ChatGPT 4o into a variable pool and structured prompts, which are used as inputs to text-to-image (T2I) generation model for generating a synthetic dataset. Object detection model is employed to detect PPE elements, and the structured outputs of object detection model are integrated with VLM, enabling vision-language reasoning that combines object detection with natural language understanding. Experimental results demonstrate that DALL·E 3 produces a more realistic synthetic dataset than other image generation models, with the hybrid dataset significantly improving detection performance ([email protected]=88.5%, Small Object [email protected]=75.8%). Using YOLOv11 detections as structured inputs, Qwen2.5-VL-7B achieves reliable compliance reasoning (CRA=87.6%, SC=0.83, EQ=4.2), and these advances are consolidated in an integrated platform supporting automated reporting and interactive analysis. This framework enhances work-at-height safety by alleviating data scarcity through generative augmentation and strengthening PPE compliance reasoning.

遵守个人防护装备是确保高空工作安全的基本先决条件。尽管计算机视觉具有先进的PPE检测，但数据集稀缺性限制了泛化，弱语义推理阻碍了可靠的符合性验证，这些方面仍然存在挑战。为了解决这些限制，本文提出了一个生成式人工智能驱动的数据增强和一个对象引导的视觉语言模型（VLM）来分析高空工作中的PPE合规性。通过ChatGPT 40将高空作业和个人防护安全标准（如GB 80-2016、GB 2811-2019）形式化为变量池和结构化提示，并将其作为文本到图像（t2c）生成模型的输入，生成合成数据集。采用目标检测模型对PPE元素进行检测，并将目标检测模型的结构化输出与VLM相结合，实现了目标检测与自然语言理解相结合的视觉语言推理。实验结果表明，与其他图像生成模型相比，DALL·e3生成的合成数据集更真实，混合数据集显著提高了检测性能（[email protected]=88.5%, Small Object [email protected]=75.8%）。Qwen2.5-VL-7B使用YOLOv11检测作为结构化输入，实现了可靠的符合性推理（CRA=87.6%， SC=0.83， EQ=4.2），并将这些进展整合到一个支持自动报告和交互式分析的集成平台中。该框架通过生成增强和加强PPE合规推理来缓解数据稀缺性，从而提高高空作业安全性。

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引用次数: 0

Signal-driven interpretable modeling framework for the inverse design of Al/steel ultrasonic-assisted resistance spot welding 铝/钢超声辅助电阻点焊反设计的信号驱动可解释建模框架

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-06 DOI: 10.1016/j.aei.2025.104295

Baokai Ren, Hao Tu, Juntao Shen, Kang Zhou

Fabricating vehicles from aluminum and steel is an effective way to balance strength with reduced weight, but the mismatch between the metallurgical and thermophysical properties of the two materials makes it difficult to achieve a reliable weld. Ultrasonic-assisted resistance spot welding (UA-RSW) has been reported to produce high-quality Al/steel joints, but the highly complex electro-thermo-mechano-acoustic system is difficult to model and thus optimize. In situ process signals from monitoring sensors offer a wealth of information on the evolution of the weld but are not directly controllable. In this study, a signal-driven interpretable modeling framework was developed that is centered on a dual-layer surrogate model. The model predicts in situ signal process features from controllable process parameters and then maps the predicted signal process features and initial process parameters to the final weld quality metrics. The model is then integrated with the non-dominated sorting genetic algorithm II to identify the Pareto front of process parameters that results in the optimal weld quality metrics. The Shapley additive explanations method is utilized to enhance the interpretability of the model. When the framework was applied to analyzing the UA-RSW process, the results indicated that ultrasonic vibrations provide a nonthermal and mechanical–metallurgical coupling effect that improves the strength of the Al/steel joint while reducing the amount of welding energy. The proposed framework offers a validated tool for transforming process signal data from monitoring sensors into actionable knowledge for the inverse design of complex manufacturing processes.

用铝和钢制造汽车是平衡强度和减轻重量的有效方法，但两种材料的冶金和热物理性能之间的不匹配使得难以实现可靠的焊接。超声辅助电阻点焊（UA-RSW）已被报道用于制造高质量的铝/钢接头，但其高度复杂的电热-热-机械-声系统难以建模和优化。来自监测传感器的现场过程信号提供了关于焊缝演变的丰富信息，但不是直接可控的。在本研究中，开发了一个以双层代理模型为中心的信号驱动的可解释建模框架。该模型根据可控制的工艺参数预测现场信号过程特征，然后将预测的信号过程特征和初始工艺参数映射到最终的焊接质量指标。然后将该模型与非支配排序遗传算法II相结合，确定工艺参数的Pareto前，从而得到最优焊接质量指标。采用Shapley加性解释方法增强模型的可解释性。将该框架应用于UA-RSW工艺分析，结果表明，超声振动提供了一种非热和机械-冶金耦合效应，提高了Al/钢接头的强度，同时降低了焊接能量。提出的框架提供了一种有效的工具，将过程信号数据从监测传感器转换为复杂制造过程逆向设计的可操作知识。

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引用次数: 0

Explainable topological-disordered 3D lattice model for hydration-temperature coupled fields of early-age concrete 早期混凝土水化-温度耦合场的可解释拓扑无序三维点阵模型

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.aei.2026.104358

Shujie Han, Xiao Zhang, Xuejiang Lan, Xiaohui Chang

The inherent structural disorder in early-age concrete significantly impacts computational efficiency and simulation accuracy in three-dimensional (3D) lattice modeling, while modeling parameters may demonstrate non-negligible effects on numerical outcomes. To achieve high-fidelity simulation of coupled hydration-temperature fields, this study proposes an automated modeling framework for topological-disordered 3D lattice structures. Parametric studies revealed that randomness and grid resolution critically influence bulk thermal conductivity. Optimal parameters were systematically determined through systematic simulation results and mechanistic interpretation of elemental heat flux distributions using automated algorithms. A macroscopic statistical relationship between modeling parameters and effective thermal conductivity of the lattice system was established by integrating stochastic analysis with the thermal resistance grid method. Validation demonstrated that this approach effectively captures modeling parametric effects on thermal conduction behavior. Finally, the proposed 3D lattice model’s capability to simulate coupled hydration-thermal fields of early-age concrete was rigorously verified against published experimental datasets under two environmental conditions. Explainable machine learning(ML) analysis was conducted on early-age temperature field simulation results of multiple concrete mixtures to investigate the influential significance of various raw material parameters on early-age temperature development. Furthermore, the parametric analysis framework and statistical methodology offer optimized solutions for disorder lattice modeling in cementitious materials, improving both computational efficiency and physical accuracy.

早期混凝土固有的结构无序性显著影响三维（3D）点阵建模的计算效率和模拟精度，而建模参数可能对数值结果产生不可忽视的影响。为了实现水化-温度耦合场的高保真仿真，本研究提出了一种拓扑无序三维晶格结构的自动建模框架。参数研究表明，随机性和网格分辨率对体导热系数有重要影响。通过系统模拟结果和自动化算法对元素热流密度分布的机理解释，系统地确定了最优参数。将随机分析与热阻网格法相结合，建立了模型参数与晶格系统有效导热系数之间的宏观统计关系。验证表明，该方法有效地捕获了建模参数对热传导行为的影响。最后，根据已发表的实验数据集，在两种环境条件下严格验证了所提出的三维晶格模型模拟早期混凝土水化-热耦合场的能力。对多种混凝土混合料早期温度场模拟结果进行可解释性机器学习（ML）分析，探讨各种原材料参数对早期温度发展的影响意义。此外，参数分析框架和统计方法为胶凝材料的无序晶格建模提供了优化的解决方案，提高了计算效率和物理精度。

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引用次数: 0

AI-supported seismic performance evaluation of structures: challenges, gaps, and future directions at early design stages 人工智能支持的结构抗震性能评估：早期设计阶段的挑战、差距和未来方向

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2026-01-30 DOI: 10.1016/j.aei.2025.104301

Fatma Ak, Berk Ekici, Ugur Demir

This study reviews 91 journal articles that intersect with earthquake-resistant building design and artificial intelligence (AI)- based modeling, utilizing machine learning, deep learning, and metaheuristic optimization algorithms. Previous reviews on AI applications have examined engineering problems without considering the impact of architectural design parameters and structural irregularities on seismic performance. This review discusses the role of AI in integrating architectural design variables and seismic performance objectives, highlighting challenges, gaps, and future directions in the early design phase. The reviewed articles demonstrate that AI is successful in addressing seismic performance objectives; however, a holistic framework for assessing architectural and structural variables has not been presented. The review highlights key findings, gaps, and future directions for those involved in earthquake-resistant building design utilizing AI.

本研究回顾了91篇与抗震建筑设计和基于人工智能（AI）的建模相关的期刊文章，这些文章利用了机器学习、深度学习和元启发式优化算法。以前对人工智能应用的评论检查了工程问题，而没有考虑建筑设计参数和结构不规则对抗震性能的影响。本文讨论了人工智能在整合建筑设计变量和抗震性能目标方面的作用，强调了早期设计阶段的挑战、差距和未来方向。回顾的文章表明，人工智能在解决地震性能目标方面是成功的；然而，评估建筑和结构变量的整体框架尚未提出。该综述强调了利用人工智能进行抗震建筑设计的主要发现、差距和未来方向。

引用次数: 0

Optimal energy management of buildings using neural network-based thermal prediction and economic model predictive control 基于神经网络热预测和经济模型预测控制的建筑能源优化管理

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2025-12-23 DOI: 10.1016/j.aei.2025.104278

Zihang Dong , Cheng Hu , Xi Zhang , Yifan Shen , Xiaojun Shen , Jose I. Leon

Heating, ventilation, and air conditioning (HVAC) systems are significant contributors to energy consumption in buildings, directly affecting energy efficiency and occupant comfort. Traditional physics-based temperature modeling and centralized control frameworks often struggle to effectively balance the challenges of scaling across multiple buildings while minimizing operational costs. To address these challenges, this paper proposes a novel data-driven distributed control framework for the economical and resilient operation of the building community. Specifically, the framework employs artificial neural networks (ANNs) to capture multi-time-step nonlinear temperature dynamics, enhancing predictive accuracy for energy management across interconnected buildings. A distributed economic model predictive control (EMPC) strategy is developed, enabling local controllers to coordinate HVAC schedules in each building iteratively. This strategy minimizes energy shortages, optimizes overall community energy costs, and ensures thermal comfort. In addition, by facilitating energy interaction between HVAC systems, distributed energy resources (DERs), and storage units, the framework ensures electrical energy supply and demand balance during power outages. Simulation results demonstrate that the proposed strategy improves cost efficiency, resilience, and multi-step prediction accuracy, outperforming traditional physics-based EMPC approaches in coordination across multiple buildings.

供暖、通风和空调（HVAC）系统是建筑物能源消耗的重要贡献者，直接影响能源效率和居住者的舒适度。传统的基于物理的温度建模和集中控制框架通常难以有效地平衡跨多个建筑物扩展的挑战，同时最大限度地降低运营成本。为了解决这些挑战，本文提出了一种新的数据驱动的分布式控制框架，用于建筑社区的经济和弹性运行。具体来说，该框架采用人工神经网络（ann）来捕获多时间步非线性温度动态，提高了跨互联建筑能源管理的预测精度。提出了一种分布式经济模型预测控制（EMPC）策略，使本地控制器能够迭代地协调各建筑的暖通空调调度。这一策略最大限度地减少了能源短缺，优化了整体社区能源成本，并确保了热舒适性。此外，通过促进HVAC系统、分布式能源（DERs）和存储单元之间的能量交互，该框架确保了停电期间的电力供需平衡。仿真结果表明，该策略提高了成本效率、弹性和多步预测精度，在跨多个建筑物的协调中优于传统的基于物理的EMPC方法。

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引用次数: 0

GridGAN-TXT: An intelligent approach to partitioning architectural free-form surfaces with text prompts GridGAN-TXT：一种智能的方法，通过文本提示来划分建筑自由形式的表面

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2025-12-23 DOI: 10.1016/j.aei.2025.104265

Jiang-Jun Hou , Jinyu Lu , Jun Zou , Binglin Lai , Haichen Zhang , Na Li

Architectural free-form surfaces have been increasingly adopted in large-scale public buildings due to their unique and aesthetically appealing forms; the geometric complexity of these surfaces, nevertheless, presents significant challenges for grid partitioning, and a universally efficient method is still lacking. To this end, a novel grid partitioning approach for architectural free-form surfaces, termed GridGAN-TXT, is proposed herein, leveraging Generative Adversarial Networks (GAN), a form of generative artificial intelligence. A distinguishing feature of GridGAN-TXT is its ability to perform grid partitioning without explicit reliance on surface characteristics. Through training data mining and learning, the model autonomously extracts relevant features, enabling automated partitioning of free-form surfaces. Moreover, GridGAN-TXT can simultaneously generate grid structures composed of triangular or quadrilateral elements (via text prompts) — a capability not supported by previous methods, which typically require totally different strategies for each element type. The technical details of GridGAN-TXT are elaborated, and a parametric strategy is proposed for constructing large-scale training datasets. Additionally, a novel grid evaluation metric — similarity evaluation — is introduced to complement the existing geometric evaluation method. The effectiveness and generalizability of GridGAN-TXT are validated through ablation studies, extensive testing, and case analyses. Results demonstrate that GridGAN-TXT exhibits exceptional performance in partitioning grids on architectural free-form surfaces and can flexibly generate grid structures with varying basic elements in response to user text prompts. Such capabilities significantly enhance the efficiency of grid partitioning while simultaneously expanding the range of potential applications, highlighting the method as a strong candidate for practical implementation.

建筑自由曲面因其独特和美观的形式在大型公共建筑中被越来越多地采用；然而，这些表面的几何复杂性对网格划分提出了重大挑战，并且仍然缺乏一种普遍有效的方法。为此，本文提出了一种用于建筑自由曲面的新型网格划分方法，称为GridGAN-TXT，利用生成对抗网络（GAN），一种生成人工智能形式。GridGAN-TXT的一个显著特征是它能够在不明确依赖于表面特征的情况下执行网格划分。该模型通过训练数据挖掘和学习，自主提取相关特征，实现自由曲面的自动划分。此外，GridGAN-TXT可以同时生成由三角形或四边形元素组成的网格结构（通过文本提示）——这是以前的方法所不支持的功能，通常需要对每种元素类型使用完全不同的策略。阐述了GridGAN-TXT的技术细节，提出了一种构造大规模训练数据集的参数化策略。此外，引入了一种新的网格评价指标——相似度评价，以补充现有的几何评价方法。GridGAN-TXT的有效性和普遍性通过消融研究、广泛的测试和案例分析得到验证。结果表明，GridGAN-TXT在建筑自由曲面上划分网格方面表现出优异的性能，并能根据用户文本提示灵活生成具有不同基本元素的网格结构。这种能力大大提高了网格划分的效率，同时扩大了潜在应用的范围，突出了该方法作为实际实现的有力候选。

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引用次数: 0

Latent diffusion–driven inverse design of damping microstructures with multiaxial nonlinear mechanical targets 多轴非线性机械目标阻尼微结构的潜扩散驱动逆设计

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2025-12-22 DOI: 10.1016/j.aei.2025.104256

Tianyang Zhang, Weizhi Xu, Shuguang Wang, Dongsheng Du

This study presents an integrated generative framework for the inverse design of damping microstructures in energy-dissipating steel walls (EDSWs) for seismic applications, establishing a seamless pipeline from large-scale pixel-based datasets to latent-space representation, three-dimensional reconstruction, industrial fabrication, and finite element analysis (FEA) verification. Starting from over 140,000 boundary-identical microstructures, a variational autoencoder-based TopoFormer compresses geometric features into latent codes, enabling over 90% reduction in generation complexity while maintaining high reconstruction fidelity. Representative structures are selected via k-means clustering in the latent space and analyzed through nonlinear FEA under shear and compression to construct a performance-labeled dataset. A conditional latent diffusion transformer (DiT) is then trained to map complete nonlinear mechanical performance curves to manufacturable geometries, thus achieving a one-to-many correspondence between target responses and structural configurations. Comparative evaluations show that the proposed DiT framework surpasses multiple CondUNet baselines, achieving the lowest FID (11.367) and the highest SSIM (0.676) with balanced coverage and precision. Experimental validation using laser-cut low-yield-point steel specimens under low-cycle reciprocating loading demonstrates close agreement between generated and target hysteresis curves, confirming both geometric fidelity and mechanical reliability. The results establish a scalable, high-accuracy, and experimentally validated approach for automated, performance-driven microstructure design, providing a practical pathway for incorporating generative artificial intelligence into the engineering development of next-generation seismic energy-dissipation systems. The related codes are available at https://github.com/AshenOneme/DiT-Based-Microstructures-Design.

本研究提出了一个集成的生成框架，用于地震应用中的耗能钢墙（EDSWs）阻尼微结构的反设计，建立了从大规模基于像素的数据集到潜在空间表示、三维重建、工业制造和有限元分析（FEA）验证的无缝管道。从超过140,000个边界相同的微结构开始，基于变分自编码器的TopoFormer将几何特征压缩为潜在代码，使生成复杂性降低90%以上，同时保持高重建保真度。在潜在空间中通过k-means聚类选择具有代表性的结构，并在剪切和压缩条件下进行非线性有限元分析，构建性能标记数据集。然后训练条件潜在扩散变压器（DiT）将完整的非线性力学性能曲线映射到可制造的几何形状，从而实现目标响应和结构构型之间的一对多对应。对比评估表明，所提出的DiT框架超越了多个CondUNet基线，实现了最低FID（11.367）和最高SSIM（0.676）的平衡覆盖和精度。激光切割低屈服点钢试样在低循环往复加载下的实验验证表明，生成的迟滞曲线与目标曲线非常吻合，证实了几何保真度和机械可靠性。研究结果为自动化、性能驱动的微观结构设计建立了一种可扩展、高精度、经过实验验证的方法，为将生成式人工智能整合到下一代地震耗能系统的工程开发中提供了一条实用途径。相关代码可在https://github.com/AshenOneme/DiT-Based-Microstructures-Design上获得。

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引用次数: 0

Intelligent control and optimization of shield tunneling machines in tunnel construction: Insights from excavation parameter data analysis and interpretable machine learning 隧道施工中盾构机的智能控制与优化：来自开挖参数数据分析和可解释机器学习的见解

IF 9.9 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2026-04-01 Epub Date: 2025-12-25 DOI: 10.1016/j.aei.2025.104270

Wen Xu , Feiming Su , Jun Liu , Xianguo Wu , Yang Liu

The complex and variable subsurface environment poses substantial challenges to the operation of shield tunneling machines (STMs). Improper excavation parameters under such conditions can increase construction costs and potentially trigger severe engineering accidents. To ensure safe and efficient tunneling in complex geological strata, this paper proposes an advanced intelligent safety decision-making system for STM posture control and optimization. The system employs a categorical boosting (CatBoost) model optimized by the Crested Porcupine Optimizer (CPO) for STM posture prediction and utilizes the SHapley Additive exPlanations (SHAP) method for model interpretability analysis. In addition, the multiobjective gray wolf optimizer (MOGWO) is applied to optimize the structural parameters. Taking the Guiyang Metro project as a case study, the applicability and effectiveness of the proposed method were comprehensively evaluated. The results show that (1) the CPO-optimized CatBoost model performs remarkably well in terms of STM posture prediction, achieving a maximum R² value of 0.927 and a minimum mean absolute percentage error (MAPE) of 2.439% across the six objective test sets. (2) Optimization of the CPO-CatBoost-based objective function with the MOGWO algorithm yields an overall STM posture improvement rate of 79.18 %. (3) The intelligent safety decision-making system not only effectively compensates for the deficiencies of the existing STM operating system but also significantly enhances the intelligence level of STM posture control. This intelligent safety decision-making system greatly strengthens the comprehensive ability of STM posture optimization and control in practical engineering applications.

复杂多变的地下环境给盾构掘进机的运行带来了巨大的挑战。在这种情况下，开挖参数的不合理会增加施工成本，并可能引发严重的工程事故。为保证复杂地质地层中隧道掘进的安全高效，提出了一种先进的STM姿态控制与优化智能安全决策系统。该系统采用冠状豪猪优化器（CPO）优化的分类提升（CatBoost）模型进行STM姿态预测，采用SHapley加性解释（SHAP）方法进行模型可解释性分析。此外，采用多目标灰狼优化器（MOGWO）对结构参数进行优化。以贵阳地铁工程为例，对该方法的适用性和有效性进行了综合评价。结果表明：(1)cpo优化的CatBoost模型在STM姿态预测方面表现优异，6个客观测试集的最大R2值为0.927，最小平均绝对百分比误差（MAPE）为2.439%。(2)采用MOGWO算法对基于cpo - catboost的目标函数进行优化，整体STM姿态改良率为79.18%。(3)智能安全决策系统不仅有效弥补了现有STM操作系统的不足，而且显著提高了STM姿态控制的智能化水平。该智能安全决策系统大大增强了STM姿态优化与控制在实际工程应用中的综合能力。

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