Advanced Engineering Informatics最新文献

{"title":"Multi-type mixed response Gaussian process with parameter estimation embedded in latent variable approximation","authors":"Zichen Wang ,&nbsp;Zhengqiang Pan ,&nbsp;Yanlin Wang ,&nbsp;Zhitao Long ,&nbsp;Zhijun Cheng ,&nbsp;Guang Jin","doi":"10.1016/j.aei.2025.103826","DOIUrl":"10.1016/j.aei.2025.103826","url":null,"abstract":"<div><div>Equipment performance experiments often collect multiple response metrics concurrently, comprising both qualitative and quantitative measures with potential complex interdependencies. However, conventional surrogate models typically fail to capture such intricate data structures. Therefore, joint modelling of multi-type mixed response data remains a critical challenge in the field of experimental design and evaluation. To address this issue, a multi-type mixed response Gaussian process model (MMRGP) is proposed in this paper, inspired by multi-output Gaussian process frameworks. The model unifies disparate data types by introducing latent variables for categorical responses and constructs a mixed-response covariance matrix to characterize inter-response correlations. However, incorporating latent variables makes parameter estimation dependent on them, while their approximate optimization is concurrently affected by model parameters. To resolve this coupled problem of parameter estimation and latent variable approximation, a bi-level optimization method is proposed that embeds latent variable approximation within parameter estimation, enabling simultaneous solving and refinement. The proposed method is validated through numerical examples and real-world datasets involving mixed responses. Comparative analyses show superior prediction accuracy and stability, with prediction errors reduced by approximately 10%–50% compared to independent modelling approaches. Finally, the MMRGP model is applied to radar anti-jamming performance experiment. The results confirm the practical efficacy and reference value for engineering applications.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103826"},"PeriodicalIF":9.9,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106494","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 dual-decoupling network for anomaly detection in automotive transmissions under time-varying operating conditions","authors":"Bin Sun ,&nbsp;Hongkun Li ,&nbsp;Aiqiang Liu ,&nbsp;Junxiang Wang ,&nbsp;Zhenhui Ma","doi":"10.1016/j.aei.2025.103859","DOIUrl":"10.1016/j.aei.2025.103859","url":null,"abstract":"<div><div>As a core component of the automotive powertrain system, the operational state of the transmission is directly related to the overall safety performance of the vehicle. However, during actual operation, transmissions are often subjected to time-varying operating conditions (TVOCs). The frequent changes in these conditions cause significant distribution shifts in the collected vibration data, leading traditional anomaly detection (AD) methods to be prone to “false positives” or “false negatives”, which compromises their engineering applicability. To address this issue, this paper proposes a novel Dual-Decoupling Network (DDN) to effectively mitigate the interference of TVOCs on AD performance, thereby enhancing detection accuracy and robustness. First, based on the causal generation mechanism of transmission vibration signals, this paper deconstructs the vibration signal into four main components: operating condition (OC) information, basic information, abnormal information, and noise information, and constructs a corresponding phenomenological model. On this basis, a DDN architecture, consisting of explicit and implicit decoupling stages, is designed. In the explicit decoupling stage, a mapping relationship between operating condition parameters and the normal vibration response is established to decouple the basic information. In the implicit decoupling stage, a novel adversarial loss is designed to further achieve deep decoupling of operating condition features and abnormal features, thereby extracting robust abnormal features that are invariant to changes in OCs. From the perspective of optimization theory, it is derived that the abnormal features extracted when the implicit decoupling model reaches its optimum are independent of the OC information. Finally, the proposed method is systematically validated on multi-level experimental platforms at the part, component, and vehicle levels, and compared with several existing advanced methods. The experimental results show that the proposed method can accurately identify and provide timely warnings for abnormal states of automotive transmissions under TVOCs, demonstrating significant potential and value for engineering applications and promotion.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103859"},"PeriodicalIF":9.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059934","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":"CBRFormer: rendering technology-based transformer for refinement segmentation of bridge crack images","authors":"Honghu Chu ,&nbsp;Jiahao Gai ,&nbsp;Weiwei Chen ,&nbsp;Jun Ma","doi":"10.1016/j.aei.2025.103868","DOIUrl":"10.1016/j.aei.2025.103868","url":null,"abstract":"<div><div>High-resolution (HR) imaging devices are crucial for ensuring the safety and efficiency of unmanned aerial vehicles (UAVs) during bridge crack detection tasks. However, due to the limitations of executing sampling discretely in traditional deep learning (DL) architectures and the constraints of GPU computing resources, it is challenging to perform fine-grained segmentation for HR crack images. To effectively address the challenge, the authors drew inspiration from the fine-grained rendering technology in the field of computer graphics (CG) and proposed the Crack Boundary Refinement Transformer (CBRFormer). Through three customized improvements, this architecture fully leverages the advantages of the rendering head in the refined representation of HR crack images. Firstly, a lightweight Transformer-based encoding architecture is designed, enabling the network to accurately capture crack backbone features from complex backgrounds. Subsequently, a boundary-guided branch based on super-resolution reconstruction technology is introduced to assist the network in capturing deep semantic information about crack boundary details. Additionally, two types of refined rendering point sampling methods are tailored for hard example areas during training and inference stages, ensuring that the prediction head used for refined rendering effectively focuses on ambiguous crack boundaries and tiny crack regions. Finally, the effectiveness of each component in the CBRFormer and the network’s practicality are demonstrated through ablation and the field experiment. Compared to the current advanced HR segmentation architectures like CascadePSP and Segfix, the CBRFormer achieved average performance improvements of 2.16% in mean Intersection over Union (IoU), 7.80% in mean Edge Accuracy (mEA), and 2.46% in Dice coefficient, respectively. The utilization of the CBRFormer enables precise segmentation of HR crack images, providing inspectors with more comprehensive and accurate structural crack information, thereby offering technical support for structural safety assessment and maintenance decision-making.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103868"},"PeriodicalIF":9.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059935","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 reinforcement learning and H∞ hybrid control framework for 0–500 Hz full-band vibration suppression in active suspension systems","authors":"Zhehui Zhu,&nbsp;Lijun Zhang,&nbsp;Chengfu Shang,&nbsp;Siqi Chen","doi":"10.1016/j.aei.2025.103844","DOIUrl":"10.1016/j.aei.2025.103844","url":null,"abstract":"<div><div>With the rapid development of intelligent vehicles, active suspension systems have become crucial for enhancing ride comfort and handling stability. However, most existing control strategies focus on low-frequency vibrations below 20 Hz while neglecting high-frequency oscillations induced by discrete control signals and hardware delays. These high-frequency vibrations can degrade system performance and compromise operational safety. To address this issue, this study proposes a dual-layer control framework integrating mixed-sensitivity H∞ robust control with a reinforcement learning (RL)-based adaptive compensator. The robust controller ensures system stability and accelerates policy convergence, whereas the RL agent provides an adaptive weighting policy that complements the robust controller in real time. A safety supervision mechanism is also incorporated to monitor control actions and override potentially unsafe outputs by reverting to robust controller commands when necessary. Furthermore, a novel 0–500 Hz vibration evaluation framework is developed to comprehensively assess suspension performance, covering conventional suspension control metrics and vibration responses within the in-cabin structure-borne noise frequency band (50–500 Hz). Simulation results on a quarter-car model with a rigid ring tire and a strut-top-mount bushing indicate that the proposed RL-H∞ method achieves a 10.42 % reduction in body acceleration from 0 to 20 Hz. Compared with pure RL-based control, it achieves a 20.11 % improvement in vibration suppression within the 50–500 Hz band. Under complex and varying road excitations, the proposed RL-H∞ controller consistently demonstrates robust vibration suppression across the 0–500 Hz frequency range. By addressing vibration responses in the in-cabin acoustic control band, this study provides a solid theoretical and methodological basis for the integrated optimization of suspension control and in-cabin road noise mitigation in intelligent vehicles.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103844"},"PeriodicalIF":9.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060695","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 systematic intelligent prediction model for residential construction cost based on fuzzy AHP and GA-BP neural network","authors":"Guangying Jin,&nbsp;Chunhui Yang","doi":"10.1016/j.aei.2025.103858","DOIUrl":"10.1016/j.aei.2025.103858","url":null,"abstract":"<div><div>Accurate and early-stage cost prediction remains a significant challenge in the construction industry due to the nonlinear, high-dimensional, and interdependent nature of influencing factors. Traditional models often rely on oversimplified assumptions or limited feature representation, leading to suboptimal accuracy and <span><span>generalization.To</span><svg><path></path></svg></span> address these gaps, this study proposes an integrated intelligent prediction framework that combines the Fuzzy Analytic Hierarchy Process (FAHP) for feature selection with a Genetic Algorithm-optimized Backpropagation Neural Network (GA-BPNN). The FAHP systematically quantifies expert judgment to identify the most relevant construction features, while the GA improves the convergence and robustness of the neural network by globally optimizing its parameters.The model was validated using a real-world dataset of 4,552 residential construction projects obtained from the Glodon platform. Results show that the FAHP-GA-BPNN framework significantly outperforms benchmark models, achieving an RMSE of 79.5, MAE of 65.2, and R² of 0.93 on the validation set. This study not only contributes a scalable and adaptable methodology for intelligent cost estimation but also offers practical insights for enhancing decision-making in residential project planning and budgeting.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103858"},"PeriodicalIF":9.9,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050165","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":"STAR: Spatial–Temporal Attention Reasoning model for dynamic logistics network routing in Cyber–Physical Internet","authors":"Zefeng Lu,&nbsp;Zhiheng Zhao,&nbsp;George Q. Huang","doi":"10.1016/j.aei.2025.103830","DOIUrl":"10.1016/j.aei.2025.103830","url":null,"abstract":"<div><div>The Cyber–Physical Internet (CPI) provides a protocol framework that enables logistics transportation to attain levels of reachability, reliability, and efficiency comparable to those of data transmission over the Internet. Within this framework, the routing problem involves determining optimal transportation routes in the logistics network by maintaining routing tables, with the objective of minimizing cost while satisfying constraints such as transportation mode and Estimated Time of Arrival (ETA). However, the inherent spatial and temporal dynamics of real-world logistics networks present substantial challenges to optimal route decision-making. On one hand, unforeseen events such as geopolitical conflicts and natural disasters render certain network nodes unavailable, altering the network topology and introducing spatial dynamics. On the other hand, the transportation time and cost of the same routes fluctuate due to changing supply–demand relationships and varying congestion levels, resulting in temporal dynamics. To tackle these uncertainties, we propose the Spatial–Temporal Attention Reasoning (STAR) model based on Reinforcement Learning (RL), which dynamically updates routing tables by leveraging the current topology and state of logistics networks. STAR uniquely combines a Topology-Aware Graph Convolutional Network (TAGCN), a Temporal-Correlated Recurrent Neural Network (TCRNN), and a Hierarchical Reward (HR) module to comprehensively capture spatial–temporal dynamics of logistics networks, thereby facilitating the adaptive decision-making of the most cost-effective routes that comply with transportation mode and ETA requirements. Numerical experiments based on real Modular-integrated Construction (MiC) cases in the Greater Bay Area (GBA) demonstrate the effectiveness of STAR in optimizing routing decisions within dynamic logistics networks.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103830"},"PeriodicalIF":9.9,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050164","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":"Meta-learning-aided generalized anomaly detection for unmanned aerial vehicles from simulation to unseen reality","authors":"Lei Yang ,&nbsp;Shaobo Li ,&nbsp;Caichao Zhu ,&nbsp;Ansi Zhang ,&nbsp;Peng Zhou ,&nbsp;Jian Liu","doi":"10.1016/j.aei.2025.103866","DOIUrl":"10.1016/j.aei.2025.103866","url":null,"abstract":"<div><div>Anomaly detection is critical to the safety and reliability of unmanned aerial vehicles (UAVs). However, traditional deep learning methods rely on independent and identically distributed (i.i.d.) assumptions and are susceptible to data distribution variations, while domain adaptation approaches are constrained by privacy and data collection costs, making it difficult to obtain target data in advance or may be inaccessible. This paper introduces a novel meta-learning-aided generalized anomaly detection (Meta-GAD) framework, which harnesses knowledge acquired from multiple simulated domains to enable robust UAV anomaly detection in real-world scenarios. First, a local–global feature joint modeling method based on one-dimensional convolutional neural network and convolutional block attention module (1D CNN-CBAM) is constructed, which leverages 1D CNN for extraction of local features and adaptively fuses local–global information via CBAM’s channel- and spatial-attention mechanisms, enhancing the model’s ability to fit complex UAV flight data. Second, a model-agnostic meta-learning (MAML) approach with a dual-gradient optimization strategy is designed, leveraging the 1D CNN-CBAM model as the base learner to learn domain-invariant representation via two gradient updates in inner-outer loops. Then, an adaptive detection strategy integrating anomaly feature enhancement and extreme distribution modeling is introduced to improve the performance of anomaly detection. Finally, the efficacy of the proposed framework is validated through model training on multiple simulated flight datasets and model testing on an unseen real flight dataset.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103866"},"PeriodicalIF":9.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049603","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":"Deep learning models for efficient geotechnical predictions: reducing training effort and data requirements with transfer learning","authors":"Haoding Xu ,&nbsp;Xuzhen He ,&nbsp;Shaoheng Dai ,&nbsp;Caihui Zhu ,&nbsp;Feng Shan ,&nbsp;Qin Zhao ,&nbsp;Faning Dang ,&nbsp;Daichao Sheng","doi":"10.1016/j.aei.2025.103852","DOIUrl":"10.1016/j.aei.2025.103852","url":null,"abstract":"<div><div>Accurate predictions of geotechnical failure loads, such as bearing capacity and slope stability, are important for safe infrastructure design and risk management. Recently, the use of ready-to-use deep-learning models as surrogate models to improve computational efficiency of risk analysis considering spatial variability has been proposed. These deep-learning models were trained with big datasets that covers all possible material properties and boundary conditions for a particular kind of problem, so it is ready to make predictions without further training. However, training such a large model requires a big dataset and substantial computational efforts. This study introduces a novel framework by employing existing deep neural networks and transfer learning techniques to effectively address these issues. Specifically, the pre-trained MobileNetV2 for image classification is used as a base. It is found that parametric ReLU (PReLU) and locally connected (LC) layers are important for our tasks. The PReLU activation can mitigate neuron deactivation by allowing the model to learn negative input slopes, while LC layers enabled the extraction of localized features – critical for accurately representing spatial variability in soil properties. Compared to a handcrafted locally connected network (MAPE ≈ 2%), the proposed deep neural network achieves similar predictive accuracy (MAPE ≈ 3%) but reduced training times (only 10% time required on the same computer). Notably, training with only 50 to 60% of the data maintained stable performance, and even with as little as 8.5% of the original dataset, satisfactory accuracy was achieved. Furthermore, this transfer learning approach generalized seamlessly to various problems without significant modifications–both bearing capacity and slope stability problems are tested. The results highlight the accuracy, efficiency, and generalization of the proposed framework, indicating the potential to simplify geotechnical engineering analyses and accelerate decision-making in practice.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103852"},"PeriodicalIF":9.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049600","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 study on product attribute identification and decision-making based on design cognitive conflicts","authors":"Hong-Yu Shao ,&nbsp;Shi-Miao Zhang ,&nbsp;Wei Guo ,&nbsp;Jiang Sun ,&nbsp;Lei Wang ,&nbsp;Zhi-Xing Chang","doi":"10.1016/j.aei.2025.103832","DOIUrl":"10.1016/j.aei.2025.103832","url":null,"abstract":"<div><div>In the rapidly evolving technological market environment, the cognitive gap between users and product management teams is increasingly widening, and the design conflicts arising during product development significantly hinder the speed and accuracy of product iterations. Meanwhile, with the advancement of intelligent technologies, online communities have become key platforms where users express their experiences and perceptions of products. Therefore, efficiently uncovering users’ implicit cognition during the design process and analyzing the cognitive conflicts between users and product management teams are of great importance for improving product design and enhancing user satisfaction. Currently, product design and optimization rarely address conflict issues between users and product management teams from a cognitive science perspective. In this study, we propose a discriminative model based on design cognitive conflicts and utilize data from China’s largest automotive online community—Autohome—for analysis. By constructing an ICA model (Importance-Conflict Analysis Model), we identify key product attributes requiring focused improvement, and subsequently generate optimal product improvement solutions based on the CPT model (Cumulative Prospect Theory Model). Theoretically, this study defines the concept of design cognitive conflict and addresses the lack of focus on the perspective of product management teams in traditional product design. Practically, the research provides guidance for product management teams in forming psychological expectations and conducting effective product design and improvement throughout the design process.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103832"},"PeriodicalIF":9.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049601","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 enhanced QUALIFLEX decision-making framework incorporating power-form scoring mechanisms within a circular intuitionistic fuzzy paradigm","authors":"Ting-Yu Chen","doi":"10.1016/j.aei.2025.103815","DOIUrl":"10.1016/j.aei.2025.103815","url":null,"abstract":"<div><div>This study introduces an enhanced QUALItative FLEXible (QUALIFLEX) decision-making framework that integrates power-form scoring mechanisms within a Circular Intuitionistic Fuzzy (CIF) paradigm. A principal innovation of this study is the formulation of parameter-driven, natural exponential-based power-form CIF scoring mechanisms that extend beyond the limitations of conventional linear aggregation models. By capturing non-linearity and interdependencies among CIF parameters, this approach enhances the reliability and robustness of decision evaluations. Additionally, this study formulates a permutation-based ranking mechanism tailored to CIF properties, reinforcing theoretical consistency while improving computational efficiency. The proposed framework integrates CIF membership, non-membership, and circular radius components into the QUALIFLEX methodology, thereby facilitating a finer-grained appraisal of alternatives under conditions of uncertainty. Furthermore, this research advances QUALIFLEX by incorporating CIF principles, refining preference modeling to enable a more comprehensive assessment of alternatives. To establish a preferential ranking, concordance–discordance metrics are applied to each dyadic comparison within the predefined preorder structure, followed by an evaluation of the overall metric across all permutations. The most suitable ranking is subsequently derived by identifying the permutation that maximizes the concordance–discordance measurement, ensuring a logically sound and robust decision outcome. Additionally, this study formulates a structured algorithmic procedure for the CIF-based QUALIFLEX methodology, ensuring systematic implementation from problem definition to optimal ranking derivation. Beyond its theoretical contributions, the present investigation probes the practical utility of CIF-QUALIFLEX within evolving decision-making arenas, particularly in assessing Artificial Intelligence (AI)-driven Clinical Decision Support System (AI-CDSS) providers. By incorporating power-form CIF scoring mechanisms into real-world scenarios, this research fosters a more resilient and adaptable QUALIFLEX-oriented decision analysis framework. Overall, this study advances CIF-based decision analytics by introducing a novel CIF-QUALIFLEX methodology that improves computational modeling, enhances ranking accuracy, and strengthens decision-making under complex uncertainty. The integration of power-form scoring mechanisms establishes a robust foundation for future developments in permutation-driven decision analysis, with significant implications for both theoretical research and practical applications.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"69 ","pages":"Article 103815"},"PeriodicalIF":9.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049602","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}