Advanced Engineering Informatics最新文献

{"title":"Multi-strategy synergistically enhanced sand cat swarm optimization: Benchmark and engineering applications","authors":"Baolong Chen,&nbsp;Fangchao Wang,&nbsp;Haocheng Wang,&nbsp;Gengchen Liu,&nbsp;Yu Zhang","doi":"10.1016/j.aei.2025.104255","DOIUrl":"10.1016/j.aei.2025.104255","url":null,"abstract":"<div><div>The Sand Cat Swarm Optimization (SCSO) algorithm, while valued for its structural simplicity, is constrained by fundamental limitations in its search mechanism — notably inefficient global exploration, excessive exploitation clustering, and inflexible phase transitions. Most existing improvement schemes rely on attaching external strategies, leading to a significant increase in computational overhead, or fail to systematically address these core bottlenecks. This study returns to the source of bio-inspiration, uncovering deeper design principles from the survival wisdom of sand cats. Guided by this philosophy, we propose a Multi-strategy Synergistically Enhanced Sand Cat Swarm Optimization (CZ-SCSO) algorithm. The core contribution lies in introducing four tightly coupled innovative strategies – the Hunger-Driven Bimodal Strategy, Intra-Population Guided Search Strategy, Neuroplasticity-Inspired Dynamic Decision Mechanism, and Natural Selection Strategy – which collectively form an efficient closed-loop optimization system. Comprehensive experiments on IEEE CEC2013 and CEC2022 benchmark functions demonstrate that CZ-SCSO significantly outperforms the original SCSO and other state-of-the-art metaheuristics in convergence accuracy, speed, and stability, achieving this superior performance without a substantial increase in computational complexity. Successful applications in constrained engineering design and real-world cases highlight CZ-SCSO’s exceptional generalization capability and practical value, presenting an efficient and effective solution to the fundamental limitations of the SCSO algorithm. The source code of the CZ-SCSO algorithm is publicly available at: <span><span>https://github.com/BaolongChen/CZ-SCSO.git</span><svg><path></path></svg></span></div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104255"},"PeriodicalIF":9.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion-driven EEG reconstruction and cognitive workload recognition using conditional diffusion and graph-based learning","authors":"Fariya Bintay Shafi ,&nbsp;Md. Faysal Ahamed ,&nbsp;Amith Khandakar ,&nbsp;Mohamed Arselene Ayari ,&nbsp;Shahriar Islam Siyam","doi":"10.1016/j.aei.2025.104243","DOIUrl":"10.1016/j.aei.2025.104243","url":null,"abstract":"<div><div>Cognitive workload recognition from EEG signals remains challenging due to real-world artifacts and missing data. To address this, we propose a unified reconstruction-classification framework that integrates EEG denoising and workload inference. Firstly, a Conditionally-Guided Denoising Diffusion Probabilistic Model (CG-DDPM) is introduced, which combines Gaussian noise modeling, a conditional encoder, and a Conditional Variational Autoencoder (CVAE) to guide a U-Net in removing diverse artifacts such as EMG, EOG, ECG, respiratory motion, powerline interference, and masked regions, while preserving essential neural activity. Secondly, an advanced classification network, EEG Graph Fusion Network (EEGGX-Net), is designed with a Hybrid Multi-Branch Encoder, a Bidirectional Multi-Head Cross Attention Fusion (MHCAF) module, and a Hierarchical Capsule Classifier (HCC) to jointly capture spatial, topological, and nonlinear dynamics of EEG signals. Both quantitative metrics (SNR: 16.50  dB, CC: 0.86, SC: 0.79) and topographic visualizations confirm CG-DDPM’s efficacy in restoring meaningful neural activity. Using a strict subject-independent 5-fold cross-validation protocol on the STEW dataset, along with external validation on the iNCog-EEG dataset, the framework achieves state-of-the-art performance in both binary and ternary settings across raw, noisy, and reconstructed conditions, exceeding 98 % and 95 % accuracy, with narrow 95 % confidence intervals confirming statistical reliability. Comparative analyses also showed statistically significant performance gains, supported by p-value evaluations across models. Ablation studies and t-SNE visualizations reaffirm robustness and generalization. These results highlight the significant potential of this unified framework for real-time cognitive workload assessment in noise-prone environments such as neuroergonomics and human–automation systems.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104243"},"PeriodicalIF":9.9,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"REACT: Runtime-Enabled active collision-avoidance technique for autonomous driving","authors":"Heye Huang ,&nbsp;Hao Cheng ,&nbsp;Zhiyuan Zhou ,&nbsp;Zijin Wang ,&nbsp;Haoran Wang ,&nbsp;Qichao Liu ,&nbsp;Xiaopeng Li","doi":"10.1016/j.aei.2025.104248","DOIUrl":"10.1016/j.aei.2025.104248","url":null,"abstract":"<div><div>Achieving rapid and effective active collision avoidance in dynamic interactive traffic remains a core challenge for autonomous driving. This paper proposes REACT (Runtime-Enabled Active Collision-avoidance Technique), a lightweight, closed-loop framework designed to unify risk assessment with interpretable active control. By leveraging energy transfer principles and human-vehicle–road interaction modeling, REACT dynamically quantifies runtime risk and constructs a spatially continuous risk field. The system incorporates physically grounded safety constraints such as directional risk and traffic rules to identify high-risk zones and generate interpretable avoidance behaviors. A hierarchical warning mechanism and lightweight runtime design ensure real-time responsiveness and onboard deployability. Evaluations across four representative high-risk scenarios including car-following braking, cut-in, rear-approaching, and intersection conflict demonstrate REACT’s capability to accurately identify critical risks and execute proactive avoidance. Its risk estimation aligns closely with human driver cognition (i.e., warning lead time &lt; 0.4  s), achieving 100 % safe avoidance with zero false alarms or missed detections. Furthermore, it exhibits low-latency performance (&lt; 50 ms latency), strong foresight, and generalization. The lightweight architecture achieves state-of-the-art accuracy, highlighting its potential for real-time deployment in safety–critical autonomous systems.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104248"},"PeriodicalIF":9.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic operational modal analysis for high arch dams using enhanced SSI-COV with adaptive MVMD and improved FCM clustering algorithm","authors":"Huokun Li ,&nbsp;Siyu Zeng ,&nbsp;Bo Liu ,&nbsp;Gang Wang ,&nbsp;Yiyuan Tang ,&nbsp;Wentao Wang ,&nbsp;Wei Huang","doi":"10.1016/j.aei.2025.104257","DOIUrl":"10.1016/j.aei.2025.104257","url":null,"abstract":"<div><div>Operational modal analysis provides critical value for structural health monitoring of arch dams, where ambient vibration-derived modal parameters characterize operational dynamic properties. This paper presents an automated method for high-fidelity identification of modal parameters. The proposed method enhances the covariance-driven stochastic subspace identification (SSI-COV) algorithm by introducing the adaptive multivariate variational mode decomposition (MVMD), the three-dimensional stabilization diagram, and an improved fuzzy c-means (FCM) clustering algorithm. Initially, the adaptive MVMD algorithm is utilized to decompose the multichannel vibration response signals. The multichannel signals are adaptively denoised by extracting and superposing sensitive signal components. Subsequently, the modal parameters of the high arch dam are obtained using the SSI-COV, and a three-dimensional stabilization diagram is developed for the automatic determination of the model order. Finally, an improved FCM clustering algorithm that integrates the local outlier factor for abnormal poles and the coati optimization algorithm for global optimization of the initial cluster centers is proposed to group physical modes, thereby facilitating automatic modal parameter estimation. Validation employs a four-DOF numerical model, a physical arch dam model, and a prototype arch dam. Results demonstrate effective noise suppression and reliable automatic modal identification under varying water discharge conditions, providing a new idea supporting continuous long-term observation of dynamic characteristics in high arch dams.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104257"},"PeriodicalIF":9.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking microstructure informatics with characterization knowledge in additively manufactured composites through customized and hybrid vision-language representations for automated qualification","authors":"Mutahar Safdar ,&nbsp;Gentry Wood ,&nbsp;Max Zimmermann ,&nbsp;Guy Lamouche ,&nbsp;Priti Wanjara ,&nbsp;Yaoyao Fiona Zhao","doi":"10.1016/j.aei.2025.104238","DOIUrl":"10.1016/j.aei.2025.104238","url":null,"abstract":"<div><div>Rapid and reliable qualification of advanced materials remains a bottleneck in industrial manufacturing, particularly for heterogeneous structures produced via non-conventional additive manufacturing processes. This study introduces a novel framework that links microstructure informatics with a range of expert characterization knowledge using customized and hybrid vision-language representations (VLRs). By integrating deep semantic segmentation with pre-trained multi-modal models (CLIP and FLAVA), we encode both visual microstructural data and textual expert assessments into shared representations. To overcome limitations in general-purpose embeddings, we developed a customized similarity-based representation that incorporates both positive and negative references from expert-annotated images and their associated textual descriptions. This allowed zero-shot classification of previously unseen microstructures through a net similarity scoring approach. Validation on an additively manufactured metal matrix composite (MMC) dataset demonstrated the framework’s ability to distinguish between acceptable and defective samples across a range of characterization criteria with up to 80% top-5 retrieval accuracy. Comparative analysis revealed that FLAVA model offers higher visual sensitivity and penalized weak similarities with score differences as large as 0.17 relative to CLIP. However, FLAVA’s text encoder exhibited sharp drops in similarity for paraphrased expert descriptions (falling below 0.20), whereas CLIP maintained more stable alignment with textual criteria (0.29–0.36). Z-score normalization adjusted raw unimodal and cross-modal similarity scores based on their local dataset-driven distributions, enabling more effective alignment and classification in the hybrid vision-language framework. The standardized scores provided strong binary classification results across three categories (82% for distribution, 90% for dilution, and 82% for reinforcement). The proposed method enhanced traceability and interpretability in qualification pipelines via human-in-the-loop decision-making without task-specific model retraining. By advancing semantic interoperability between raw data and expert knowledge, this work contributes toward scalable and domain-adaptable qualification strategies in engineering informatics.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104238"},"PeriodicalIF":9.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of LiDAR scan-to-IFC and UWB real-time positioning for automated construction monitoring: a precast module case study","authors":"Maggie Y. Gao,&nbsp;Chengjia Han,&nbsp;Zhen Peng,&nbsp;Yiqing Dong,&nbsp;Robert L.K. Tiong,&nbsp;Yaowen Yang","doi":"10.1016/j.aei.2025.104247","DOIUrl":"10.1016/j.aei.2025.104247","url":null,"abstract":"<div><div>This paper presents a novel framework for construction monitoring, focusing on efficient as-built model registration through the integration of LiDAR scanning and Ultra-Wideband (UWB) positioning technology. The proposed approach leverages UWB positioning data as preliminary spatial references for precise alignment of as-built model for precast module and components from LiDAR point cloud processing. This integrated framework addresses the critical gap in construction monitoring by integrating multiple technologies into a cohesive system, overcoming the limitations of fragmented approaches. During these procedures, the study presents a systematic targeted partial transformation to correct angular misalignments during point cloud registration. This framework employs BIMCrossNet, a custom deep learning architecture specifically designed for point cloud segmentation in construction scenarios. At last, the study enables automated updating of semantic enriched as-built BIM models with real-time validation of component placement, making it particularly valuable for quality control and progress monitoring in modular construction applications.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104247"},"PeriodicalIF":9.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-data driven model for explosion-induced crack identification in high arch dams","authors":"Junwei Xiong ,&nbsp;Wenbo Lu ,&nbsp;Gaohui Wang ,&nbsp;Yufeng Huang ,&nbsp;Zhidong Liu","doi":"10.1016/j.aei.2025.104129","DOIUrl":"10.1016/j.aei.2025.104129","url":null,"abstract":"<div><div>The identification of explosion-induced damage in high arch dams is a crucial aspect in the anti-explosion safety evaluation of such structures. This study proposes a knowledge-data driven framework for identifying explosion-induced cracks in high arch dams. The model formalizes underlying physical knowledge pertaining to modal evolution law in cracked arch dams and integrates the efficient nonlinear computational capabilities of machine learning, enabling accurate and efficient crack identification. First, quantitative damage identification indices, including modal frequency change and mode localization factor, are introduced based on the theory of mode transition and mode localization. A modal sequence reconstruction method is developed to establish a data preprocessing framework considering modal transition. Building upon this, a modal parameter prediction model, using a human evolutionary optimization algorithm-based support vector regression (HEOA-SVR), replaces the finite element model (FEM) in the sampling process. Additionally, Bayesian theory is integrated to form a knowledge-data driven model for explosion crack identification in high arch dams. Finally, a modal database for explosive-damaged arch dam is established based on one arch dam, and the effectiveness of this damage identification model is validated through the identification of horizontal cracks. The results demonstrate that the identification indices based on modal frequency and beamwise mode localization factor offer superior accuracy and noise immunity for horizontal crack detection, aligning with physical knowledge. In comparison with the methods that neglect mode transition and mode localization, the knowledge-data driven model improves recognition efficiency while maintaining high accuracy and precision.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104129"},"PeriodicalIF":9.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remaining useful life prediction based on self-attention mechanism -sequential variational autoencoder: From a semi-supervised perspective","authors":"Jiusi Zhang ,&nbsp;Kai Chen ,&nbsp;Fan Wu ,&nbsp;Quan Qian ,&nbsp;Tenglong Huang ,&nbsp;Yuhua Cheng ,&nbsp;Shen Yin","doi":"10.1016/j.aei.2025.104242","DOIUrl":"10.1016/j.aei.2025.104242","url":null,"abstract":"<div><div>Remaining useful life (RUL) prediction is a crucial task in maintaining operational safety and dependability of complex systems. Considering that conventional data-driven RUL prediction approaches demand massive labeled samples for supervised model training, it has notable limitations in making full use of unlabeled degradation data. Furthermore, current deep networks require in-depth research work in terms of interpretability and uncertainty. In this sense, an RUL prediction approach with the self-attention mechanism-sequential variational autoencoder (SAM-SVAE) is proposed from a semi-supervised perspective. Specifically, considering a dynamic serialization modeling, this paper designs a self-attention mechanism network to focus on key parts of the input time window. On this basis, this paper explores the correspondence between a Bayesian deep probability generation network and the state space model in control theory, which approximates the RUL prediction’s density function for uncertainty assessment. Moreover, this paper proposes an SAM-SVAE from a semi-supervised perspective, which can learn valuable feature representations from a large amount of unlabeled degradation data, from which the interpretability is provided through the analyze of latent space. Experimental validation of the presented SAM-SVAE utilizes the aircraft turbofan engine dataset from NASA Prediction Center.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104242"},"PeriodicalIF":9.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of condition monitoring approaches for ball screws","authors":"Himanshu Gupta ,&nbsp;Tauheed Mian ,&nbsp;Pradeep Kundu","doi":"10.1016/j.aei.2025.104206","DOIUrl":"10.1016/j.aei.2025.104206","url":null,"abstract":"<div><div>Ball screws are critical components primarily used in feed drives for machine tools and manufacturing systems, as well as in electromechanical actuators (EMAs). These are linear motion systems, enabling the conversion of rotary motion into precise linear motion. The performance and efficiency of these motion systems heavily rely on the safe operation of the ball screws. Their failures can result in costly downtime or catastrophic consequences. Condition monitoring (CM) approaches have been developed to detect these failures on time and estimate their remaining useful life (RUL). Although these approaches have been extensively reviewed for components like bearings, gears, and motors, a comprehensive evaluation explicitly focused on ball screw systems is still lacking. This paper addresses this gap by presenting an in-depth review of existing approaches for ball screw CM. It examines the different ball screw failure modes and physics-based, data-driven, and hybrid approaches for their CM. Further, the challenges associated with the existing approaches are discussed, along with potential solutions and future research directions.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104206"},"PeriodicalIF":9.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An hourglass-shaped digital twin framework for industrial computed tomography","authors":"Zhiyu Gao ,&nbsp;Haibin Lan ,&nbsp;Changsheng Zhang ,&nbsp;Qianni Wang ,&nbsp;Shengping Yuan ,&nbsp;Wenlei Xiao ,&nbsp;Gang Zhao ,&nbsp;Jian Fu","doi":"10.1016/j.aei.2025.104251","DOIUrl":"10.1016/j.aei.2025.104251","url":null,"abstract":"<div><div>Industrial computed tomography (ICT) is one of the most advanced non-destructive testing, evaluation and characterization techniques, used for revealing internal structures, analyzing material compositions, and identifying defects within objects. However, in practical engineering applications, it has encountered challenges such as complex operation procedures and parameter settings, high costs, as well as artifacts and measurement errors. Digital twin (DT), by creating a virtual environment that closely mirrors and interacts with the physical space, offers an innovative solution to these challenges. In this paper, an hourglass-shaped DT framework for ICT is presented. By establishing both static and dynamic connections between the physical and virtual spaces, the ICT system is twinned across three dimensions: entities, behaviors, and data. This integration enables the applications and functions layer to improve the efficiency and accuracy of the ICT process. The implementation of the DT framework within the ICT system is explained in detail, and a case study of a workpiece’s ICT inspection process is used to validate its feasibility and effectiveness. This work has been helpful in advancing the digitization of the industrial inspection field under Industry 4.0.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"71 ","pages":"Article 104251"},"PeriodicalIF":9.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145792147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}