Advanced Engineering Informatics最新文献

{"title":"AIoT-powered building digital twin for smart firefighting and super real-time fire forecast","authors":"Weikang Xie ,&nbsp;Yanfu Zeng ,&nbsp;Xiaoning Zhang ,&nbsp;Ho Yin Wong ,&nbsp;Tianhang Zhang ,&nbsp;Zilong Wang ,&nbsp;Xiqiang Wu ,&nbsp;Jihao Shi ,&nbsp;Xinyan Huang ,&nbsp;Fu Xiao ,&nbsp;Asif Usmani","doi":"10.1016/j.aei.2025.103117","DOIUrl":"10.1016/j.aei.2025.103117","url":null,"abstract":"<div><div>Complex dynamics inherent of building fire poses big challenges to firefighting and rescue, especially with limited access to critical fire-hazard information. This work proposes the novel AIoT-integrated Digital Twin for the full-scale multi-floor building to manage the dynamics fire information. This system allows for super real-time mapping of actual building fires into accurate and concise digital fire scene at the cloud platform. By developing the ADLSTM-Fire model, we effectively transform discrete sensor-array data into high-dimensional spatiotemporal temperature fields in real-time, and furthermore, forecast future fire development and hazardous regions 60 s in advance. By comparing with benchmark numerical simulations, the Digital Twin system demonstrates the high reliability of super real-time fire-scene reconstruction and the capacity of fire-risk forecasting in supporting firefighting. The full-scale building fire experiment is employed to validate the generalisation capability of the proposed smart firefighting method. This work demonstrates the great potential and robustness of AIoT and digital twin in support smart firefighting and reducing fire casualties by information fusion.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103117"},"PeriodicalIF":8.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic pose measurement of robotic drilling system based on zoom monocular vision","authors":"Bowen Yang ,&nbsp;Xuexiang Cen ,&nbsp;Luofeng Xie,&nbsp;Ming Yin","doi":"10.1016/j.aei.2025.103121","DOIUrl":"10.1016/j.aei.2025.103121","url":null,"abstract":"<div><div>Robotic drilling systems are attracting more attention due to their excellent processing accessibility and manufacturing flexibility, enabling in-situ processing of large-scale components. However, due to lack of feedback mechanism, it is difficult to achieve the required precision in hole-making positions. Although a laser tracker can be used to determine the positional deviation of the robotic drilling system with respect to the hole, accurately measuring the pose deviation still remains a formidable challenge. To tackle this issue, a sophisticated pose measurement system is proposed, which is composed of a zoom camera and a stereo cooperative target. To ensure that the pose can be effectively measured over a large range of distances, an automatic zoom calibration method based on Huber regression is proposed. Moreover, to establish the correspondence between the 3D target feature point coordinates and the 2D image feature coordinates, a novel automatic pose estimation algorithm is designed, which addresses the problem of matching failure for conventional pose estimation algorithms. Experimental results demonstrate that our pose measurement system can effectively complete the pose measurement task, with a measurement accuracy of 0.04° ranging from 3 to 7 m.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103121"},"PeriodicalIF":8.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137075","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":"Loading optimization of mixed-type containers for double-stack trains in multi-hub logistics","authors":"Zhongbin Zhao ,&nbsp;Jihong Chen ,&nbsp;Mengru Shen ,&nbsp;Zheng Wan ,&nbsp;Hao Wang ,&nbsp;Linlan Yu","doi":"10.1016/j.aei.2025.103128","DOIUrl":"10.1016/j.aei.2025.103128","url":null,"abstract":"<div><div>Electrified double-stack container trains (DSTs) play a crucial role in modern logistics by offering increased capacity per trip, reduced rail car usage, lower transportation costs, and fewer emissions. However, optimizing container loading for DSTs is challenging due to constraints such as height limits, center of gravity balance, and other operational requirements. This paper introduces a mixed-integer programming (MIP) model aimed at maximizing transportation efficiency in multi-hub logistics networks, which include intermodal terminals and freight stations. The model supports mixed loading of containers of varying lengths (20/40/48 feet), heights (standard/high cube), load statuses (empty/loaded), and types (regular/foldable), originating from and destined for different locations. Additionally, it incorporates the combination of double-stack container well cars with other rail car types, increasing flexibility in rail car organization and accelerating DST departure times. To solve the complex loading problem, a hybrid genetic algorithm combined with simulated annealing (hybrid GA-SA) is developed. The hybrid GA-SA demonstrates strong performance in numerical case studies across different scales, significantly reducing the number of rail cars needed for large-scale logistics operations while achieving optimal loading configurations. Sensitivity analysis highlights key factors influencing overall transportation benefits. This study offers practical insights for enhancing the operational efficiency and profitability of DSTs and improving container hub throughput within modern logistics networks.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103128"},"PeriodicalIF":8.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143137067","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":"Prediction of seam tracking errors in the intelligent welding system: A rapid prediction method based on real-time monitoring data","authors":"Gang Shang ,&nbsp;Liyun Xu ,&nbsp;Zufa Li ,&nbsp;Lizhen Xiao ,&nbsp;Zhuo Zhou ,&nbsp;Hanwu He","doi":"10.1016/j.aei.2025.103124","DOIUrl":"10.1016/j.aei.2025.103124","url":null,"abstract":"<div><div>In the field of intelligent welding, using industrial robots to track complex shaped welds is a challenging task. When welding complex seams, the welding tools carried by industrial robots often deviate from the expected center of the weld seams. Especially during thin plate welding, thin plates are prone to random deformation because of uneven heating, which makes automatic seam tracking more difficult. The proposal of the predictive compensation control strategy for seam tracking errors provides a new approach for intelligent seam tracking. For this new approach, the rapid and accurate prediction of seam tracking errors is an important prerequisite for improving the efficiency and accuracy of the intelligent compensation system. To this end, a time-delay recursive discrete grey model (TRDGM) is proposed to predict seam tracking errors in real time. We used the new information priority accumulated generating operation (NIPAGO) to establish a time-delay grey model, and incorporated the recursive least squares method and sparrow search algorithm (SSA) to automatically optimize the parameters of the TRDGM. The prediction performance of the TRDGM was tested by seam tracking error data, which was collected during the process of thin plate automatic welding. According to industrial application requirements, one-step prediction and three-step prediction experiments were conducted. This method was compared with several typical grey models and machine learning methods. The effect of sample size on the TRDGM stability was also investigated. The results show that the TRDGM has better prediction accuracy and stability than the existing methods under small sample conditions. The TRDGM can meet the real-time requirements of automatic control, and its solution time is approximately 0.4 s with a sample size of 80. Meanwhile, the TRDGM can adapt to changes in sample size and performs well in both small and medium sample predictions. The seam tracking experiments show that compared with other prediction methods, TRDGM helps to reduce tracking errors. Based on the real-time monitoring and accurate prediction of seam tracking errors, potential welding risks can be distinguished. On this basis, it can provide operational guidance for industrial robotics to improve the accuracy of automatic seam tracking and welding quality.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103124"},"PeriodicalIF":8.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136979","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":"A feature curve-based method for balancing brand identity and emotional imagery in automobile frontal form design","authors":"Peng Lu ,&nbsp;Fan Wu ,&nbsp;Shih-Wen Hsiao ,&nbsp;Jian Tang","doi":"10.1016/j.aei.2025.103118","DOIUrl":"10.1016/j.aei.2025.103118","url":null,"abstract":"<div><div>Automobile frontal forms are crucial for conveying form imagery and inheriting brand identity. However, few studies have balanced both brand features and form imagery. This research introduces a method for blending and recombining feature curves to achieve this balance. This method constructs a form database by extracting the form curves from numerous car frontal images and setting target imagery based on designers’ evaluations. Consumer perceptual questionnaires are then used to select base and reference forms from the database, which are decomposed into paired feature curves. Subsequently, new feature curves are generated using an improved ray-firing method and form blending algorithm. Three groups of form curves (group_1, group_2 and group_3) are created as alternatives through three recombination methods (method_1, method_2 and method_3) and converted into 3D renderings using image-generative AI. Finally, the alternatives are evaluated for brand form feature inheritance, form imagery transfer, and form aesthetics using the AHP, quadratic curvature entropy, and perceptual questionnaires. Results show that blending and recombining feature curves can effectively balance brand identity and emotional imagery, with quadratic curvature entropy serving as a reliable metric for assessing form aesthetics. This research offers an innovative approach to automobile form design, contributing to the advancement of the automotive industry.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103118"},"PeriodicalIF":8.0,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136976","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":"A data-driven approach to solving the container relocation problem with uncertainties","authors":"Zhanluo Zhang ,&nbsp;Kok Choon Tan ,&nbsp;Wei Qin ,&nbsp;Ek Peng Chew ,&nbsp;Yan Li","doi":"10.1016/j.aei.2025.103112","DOIUrl":"10.1016/j.aei.2025.103112","url":null,"abstract":"<div><div>Container relocations are inevitable and reduce terminal efficiency, making their optimization a critical research focus. Scholars have extensively studied the Container Relocation Problem (CRP) with the goal of reducing relocations. However, most existing research assumes prior knowledge of retrieval sequences, which often does not reflect real-world conditions. Consequently, addressing the CRP with uncertain retrieval sequences necessitates overcoming challenges related to both uncertainty and complexity. To manage this uncertainty, we propose a novel concept: the Retrieval Probability Matrix (RPM). A data-driven model is developed to predict the RPM, utilizing real terminal operational records. Building on this foundation, this study extends the online CRP to the Probabilistic Container Relocation Problem (PCRP) and presents a decision tree-based algorithm for obtaining optimal solutions. To address the inherent complexity of the PCRP, we propose an Adapted Monte Carlo Tree Search algorithm. It minimizes the expected number of container relocations by integrating a novel heuristic: Local Safety and Global Flexibility. The proposed algorithms are validated through experiments, demonstrating their effectiveness and feasibility. Furthermore, sensitivity analysis is conducted to evaluate the impact of RPM prediction accuracy on algorithm performance.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103112"},"PeriodicalIF":8.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136978","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":"A deep learning framework for real-time multi-task recognition and measurement of concrete cracks","authors":"Gang Xu ,&nbsp;Yingshui Zhang ,&nbsp;Qingrui Yue ,&nbsp;Xiaogang Liu","doi":"10.1016/j.aei.2025.103127","DOIUrl":"10.1016/j.aei.2025.103127","url":null,"abstract":"<div><div>This study presents an innovative deep learning framework, YOLO-DL, for automatic multi-task recognition of concrete cracks. The framework integrates the You Only Look Once (YOLO) object detection algorithm with the encoder-decoder architecture of the DeepLabv3 + model, incorporating an attention mechanism and a calibration module, resulting in three distinct branches for crack classification, localization detection, and semantic segmentation. The YOLO-DL model achieves a detection precision of 84.87 %, an [email protected] of 83.55 %, and a mean intersection-over-union (mIoU) of 94.94 % for crack segmentation. The model’s segmentation inference time is significantly shorter than that of the DeepLabv3+, fully convolutional networks (FCN), U-Net, and SegNet models, making it suitable for real-time concrete crack recognition. The model effectively handles classification, detection, and segmentation tasks, demonstrating enhanced performance and robustness, particularly with the inclusion of the attention mechanism. Additionally, a novel crack width measurement method based on the local element grid method is presented, achieving sub-pixel precision. This method provides comprehensive crack width information, including the maximum width of each crack and its corresponding location, with a maximum relative error of less than 10 %. The findings highlight the model’s strong inference performance, robust generalization ability, and promising real-time crack recognition capabilities.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103127"},"PeriodicalIF":8.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136940","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":"Multi-unit global-local registration for 3D bent tube based on implicit structural feature compatibility","authors":"Le Wang ,&nbsp;Zili Wang ,&nbsp;Shuyou Zhang ,&nbsp;Jianrong Tan ,&nbsp;Yaochen Lin ,&nbsp;Yongzhe Xiang","doi":"10.1016/j.aei.2025.103120","DOIUrl":"10.1016/j.aei.2025.103120","url":null,"abstract":"<div><div>Point cloud registration for evaluating the shape of 3D bent tubes is a preferred method for improving the forming quality and reducing fabrication costs. In this process, large nonlinear deformations, smooth regions, and low overlap result in massive outliers, making accurate registration for forming iterative optimization a challenging yet indispensable technique. We propose a new registration method based on implicit structural feature compatibility to predict the global-local rigid transform for multi-unit 3D bent tubes, called ISFC. In the two-stage tactic of ISFC for the alignment of the cross-source point cloud, the rigid compatibility in overlap regions and non-rigid compatibility in deformation regions are discriminated by the soft-distance consistency metric for global correspondence initialization. A new implicit axial structure constraint is established by evolution from the surface point to the interior based on the grassfire analogy, which joins faithful anchor points to generate a robust global correspondence hypothesis. Based on the global pose transformation, an innovative multipliers method named PC-ADMM is proposed for sequential local registration, which introduces a processing constraint into the optimization objective of the Lie group to refine tube unit transformation. The robustness and accuracy of the proposed method are confirmed by extensive registration experiments on synthetic and real-world tube datasets.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103120"},"PeriodicalIF":8.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136977","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":"Research on multimodal generative design of product appearance based on emotional and functional constraints","authors":"Zeng Wang ,&nbsp;Jiang-shan Li ,&nbsp;Hui-ru Pan ,&nbsp;Jun-yun Wu ,&nbsp;Wei-an Yan","doi":"10.1016/j.aei.2024.103106","DOIUrl":"10.1016/j.aei.2024.103106","url":null,"abstract":"<div><div>The latest advancements in generative design have unveiled its potential in converting textual inputs into conceptual renderings, yet challenges remain in aligning these designs with users’ emotional and functional requirements. Additionally, research on integrating text, renderings, and 3D models within a multimodal product design framework is still inadequate. Consequently, this study introduces a novel multimodal generative design approach based on emotional and functional constraints. Firstly, a dataset of product emotional and functional vocabularies is constructed and utilized to train a stable diffusion model enhanced by LoRA. Subsequently, a Multi-stage Fuzzy Comprehensive Evaluation and Analytic Hierarchy Process are employed to assess the design proposals generated by the pre-trained model based on user requirements. Ultimately, the preferred 2D renderings are converted into detailed 3D models using the One-2–3-45++ method, with a case study on seat design validating its effectiveness. The primary contribution of this study lies in proposing a generative design method that accurately maps users’ personalized emotional and functional requirements to product styling, color, structure, and material, while also establishing a comprehensive and intelligent multimodal generative design framework incorporating a comprehensive evaluation system. Compared to existing methods, the proposed approach demonstrates superior performance in satisfying users’ emotional and functional requirements, significantly enhancing the personalization level of generative product design.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103106"},"PeriodicalIF":8.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136927","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":"Physics-data driven multi-objective optimization for parallel control of TBM attitude","authors":"Limao Zhang,&nbsp;Yongsheng Li,&nbsp;Lulu Wang,&nbsp;Jiaqi Wang,&nbsp;Hui Luo","doi":"10.1016/j.aei.2024.103101","DOIUrl":"10.1016/j.aei.2024.103101","url":null,"abstract":"<div><div>To more accurately control the attitude of the tunnel boring machine (TBM), this study proposes a physics-data driven multi-objective optimization (MOO) method. The proposed method combines the dynamics theory of the shield propulsion hydraulic system with deep neural networks (DNN) to generate a physics-informed deep learning (PIDL) model that is capable of accurately estimating oil cylinder strokes. Furthermore, a simulation model integrating the PIDL and the non-dominated sorting genetic algorithm III (NSGA-III) is established to perform optimization of shield attitude deviation. A field test of synchronous excavation and segment assembly TBM (S-TBM) is used as a case study to confirm the proposed method’s reliability. The results indicate that: (1) The developed PIDL model accurately predicts oil cylinder strokes under different geological conditions with <em>R</em>² values of 0.99. (2) For all strata, the proposed shield attitude control framework achieves an average overall improvement rate of 19.57% while considering regulation time, overshoot, and accumulative error simultaneously. (3) The proposed PIDL stands out with an advantage of 0.40 higher <em>R</em>² mean value than that of existing methods. (4) Compared to other popular MOO algorithms, the NSGA-III employed in this study generates Pareto fronts with the highest hypervolume mean value of 7.25, demonstrating better convergence and diversity. The novelty of this study lies in proposing an optimization framework with the integration of PIDL, NSGA-III, and virtual model to realize effective control of shield attitude.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"65 ","pages":"Article 103101"},"PeriodicalIF":8.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136930","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}