alexandria engineering journal最新文献_第2页

Fracture evolution and anisotropic mechanical properties of layered rock based on discrete element modeling and experimental study 基于离散元模型与实验研究的层状岩石裂缝演化与各向异性力学特性

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-02-01 DOI: 10.1016/j.aej.2026.01.042

Minglang Zou , Yan Zhang , Shaojun Li , Tianbin Li , Guoqiang Zhu , Yining Zhang

Since numerous geotechnical activities crossing layered rock masses, a comprehensive understanding of their mechanical behavior is crucial for engineering stability assessment. This paper establishes transversely isotropic numerical models with multiple bedding angles using PFC3D. After calibrating the meso-parameters to verify model validity, multi-confining pressure triaxial compression simulations are conducted. The results show that both peak strength and elastic modulus exhibit a “U” shaped with bedding angle increase, divided at 60°; under the same confining pressure, the maximum differences reaching 42.2 % and 26.6 %, respectively; failure characteristics vary significantly with bedding angle, manifesting as axial splitting (0°), shear sliding along bedding planes (30°–60°), and mixed tension-shear failure (90°). The sudden increase in acoustic emission (AE) event count and energy can serve as reliable precursors of rock failure, while the proposed energy competition factor β (ratio of slip energy to bond energy) can effectively characterize rock failure evolution. CT observations indicate that confining pressure suppresses micro-crack initiation and propagation. Tunnel excavation simulations based on discrete element method further demonstrate that bedding angle plays a significant controlling role in surrounding rock deformation patterns and support requirements. The research findings provide important insights for stability assessment and support design in layered rock masses.

由于许多岩土工程活动都是跨越层状岩体的，因此全面了解其力学行为对工程稳定性评估至关重要。本文利用PFC3D建立了具有多层理角度的横向各向同性数值模型。在标定细观参数验证模型有效性后，进行了多围压三轴压缩模拟。结果表明：随着层理角的增大，峰值强度和弹性模量均呈“U”形，在60°处划分；在相同围压下，最大差异分别达到42.2% %和26.6% %；破坏特征随层理角度变化显著，表现为轴向劈裂（0°）、剪切沿层理面滑动（30°~ 60°）和拉剪混合破坏（90°）。声发射事件数和能量的突然增加可以作为岩石破坏的可靠前兆，而提出的能量竞争因子β（滑移能与键能之比）可以有效地表征岩石破坏演化。CT观察表明，围压抑制了微裂纹的萌生和扩展。基于离散元法的隧道开挖模拟进一步证明了顺层倾角对围岩变形模式和支护要求具有重要的控制作用。研究结果对层状岩体稳定性评价和支护设计具有重要的指导意义。

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引用次数: 0

Numerical treatments of (IntVarFrac) order partial differential equations for cancer tumor disease based on non-singular kernel 基于非奇异核的肿瘤疾病（IntVarFrac）阶偏微分方程的数值处理

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-02-01 DOI: 10.1016/j.aej.2026.01.015

N.H. Sweilam , S.M. Al–Mekhlafi , A. Ahmed , E.M. Abo-Eldahab , Nehaya R. Alsenaideh

Cancer remains one of the most challenging medical conditions, requiring sophisticated mathematical models to accurately describe its dynamics and treatment responses. Traditional integer-order differential equations often fail to capture the complexities of tumor growth, immune system interactions, and therapeutic effects. In this study, we propose an advanced cancer tumor model based on integer-variable-fractional order partial differential equations with nonsingular kernels. This model incorporates memory dependent properties to more effectively represent the biological behavior of tumor progression under chemotherapy. Using the Atangana–Baleanu Caputo fractional operator, we extend the classical system of coupled partial differential equations to an IntVarFrac framework. The theoretical analysis of the proposed model is presented, and a numerical scheme based on the Atangana–Baleanu fractional Newton polynomial method is employed to obtain approximate solutions. Numerical simulations illustrate the influence of fractional-order parameters on tumor dynamics and the effectiveness of chemotherapy treatment. The results demonstrate that the inclusion of memory effects through fractional derivatives provides a more accurate. Although the model has not yet been validated with clinical tumor data, it captures key tumor–immune dynamics reported in the literature, supporting its biological relevance.

癌症仍然是最具挑战性的医疗条件之一，需要复杂的数学模型来准确描述其动态和治疗反应。传统的整阶微分方程往往无法捕捉肿瘤生长、免疫系统相互作用和治疗效果的复杂性。在这项研究中，我们提出了一个基于非奇异核的整变分数阶偏微分方程的晚期癌症肿瘤模型。该模型结合了记忆依赖特性，以更有效地代表化疗下肿瘤进展的生物学行为。利用Atangana-Baleanu Caputo分数算子，将经典的耦合偏微分方程组推广到IntVarFrac框架。对所提出的模型进行了理论分析，并采用基于Atangana-Baleanu分数阶牛顿多项式方法的数值格式来求得近似解。数值模拟说明了分数阶参数对肿瘤动力学和化疗效果的影响。结果表明，通过分数阶导数包含记忆效应提供了一个更准确的。尽管该模型尚未得到临床肿瘤数据的验证，但它捕获了文献中报道的关键肿瘤免疫动力学，支持其生物学相关性。

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引用次数: 0

Efficient human pose estimation in complex coal mining scenes via Keypoint Partitioning Adaptive Convolution 基于关键点划分自适应卷积的复杂煤矿场景人体姿态估计

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-02-01 DOI: 10.1016/j.aej.2026.01.041

Jin Wu , Huaping Zhou , Xiangrui Meng , Tao Wu

Human pose estimation (HPE) is crucial for underground mining safety, but it suffers from uneven brightness, occlusions from dense equipment, complex backgrounds, and limited computational resources. To address these challenges, we propose a novel Keypoint-Adaptive Convolutional Network (KAnet) for accurate miner pose estimation. KAnet integrates our newly proposed content-adaptive convolution method called Keypoint Partitioning Adaptive Convolution (KAconv), which adaptively partitions feature maps based on semantic similarity and generates region-specific dynamic filters. This design enables the model to handle complex and variable spatial information distribution effectively. Additionally, we introduce an Attention-Based Cross-Layer Feature Fusion (ACFF) module to enhance multi-scale feature fusion and improve robustness against occlusion and illumination variations. To further optimize model efficiency, we present the Pruning-guided Adaptive Filtering Knowledge Distillation (PAF-KD), which leverages channel importance ranking for efficient model compression while preserving essential feature representations. We validate the effectiveness of KAnet using the newly developed Miner-Pose dataset, a large-scale dataset of miner poses in coal mines. Experimental results demonstrate that KAnet outperforms current state-of-the-art methods in both accuracy and robustness in complex mining scenarios.

人体姿态估计（HPE）对地下采矿安全至关重要，但存在亮度不均匀、设备密集遮挡、背景复杂、计算资源有限等问题。为了解决这些挑战，我们提出了一种新的关键点自适应卷积网络（KAnet）来精确估计矿工姿态。KAnet集成了我们新提出的内容自适应卷积方法关键点划分自适应卷积（KAconv），该方法基于语义相似度自适应划分特征映射并生成特定区域的动态过滤器。这种设计使模型能够有效地处理复杂多变的空间信息分布。此外，我们引入了一个基于注意力的跨层特征融合（ACFF）模块来增强多尺度特征融合，并提高对遮挡和光照变化的鲁棒性。为了进一步优化模型效率，我们提出了剪枝引导的自适应滤波知识蒸馏（PAF-KD），它利用信道重要性排序进行有效的模型压缩，同时保留基本特征表示。我们使用新开发的miner - pose数据集验证了KAnet的有效性，这是一个煤矿矿工姿势的大规模数据集。实验结果表明，在复杂的采矿场景中，KAnet在准确性和鲁棒性方面都优于当前最先进的方法。

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引用次数: 0

TraceNet: A novel modular framework for robust Multi-Object Tracking in crowded and dynamic environments TraceNet：一种新颖的模块化框架，用于在拥挤和动态环境中进行鲁棒的多目标跟踪

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-02-01 DOI: 10.1016/j.aej.2026.01.032

Said Baz Jahfar Khan , Peng Zhang , Mian Muhammad Kamal , Abdullah Alharbi , Amr Tolba , Muhammad Sheraz , Teong Chee Chuah

Multi-Object Tracking (MOT) is a fundamental task in computer vision, vital for applications in autonomous driving, intelligent surveillance, and sports data analysis. However, tracking performance significantly degrades under conditions such as occlusion, small object instances, and fast motion. This work proposes TraceNet, a modular multi-object tracking framework designed to address these challenges by incorporating sophisticated detection, association, and recovery components. TraceNet builds on a fine-tuned YOLOv11 detector and incorporates a Confidence Optimization Network (CON) to improve detection reliability in low-visibility environments. It further includes a Deep Similarity Integration (DSI) module improved by Dynamic IoU Adjustment (DIA), which combines motion prediction and appearance cues to achieve reliable identification associations. The framework uses a Graph-Based Track Recovery (GBTR) network and a Neural Trajectory Smoother (NTS) to recover interrupted trajectories and ensure temporal consistency. The temporal association is further enhanced by a Transformer-Based Association (TBA) module. TraceNet achieves exceptional performance on four challenging benchmarks, achieving HOTA scores of 66.9 for MOT17 and 66.7 for MOT20, with IDF1 scores of 83.2 and 83.5, respectively. These results highlight TraceNet’s robustness in dense and occluded scenes, and demonstrate that it is a high-performing and scalable solution for real-time multi-object tracking.

多目标跟踪（MOT）是计算机视觉中的一项基本任务，对于自动驾驶、智能监控和体育数据分析的应用至关重要。然而，在遮挡、小对象实例和快速运动等条件下，跟踪性能会显著下降。这项工作提出了TraceNet，一个模块化的多目标跟踪框架，旨在通过结合复杂的检测、关联和恢复组件来解决这些挑战。TraceNet建立在经过微调的YOLOv11探测器上，并结合了置信度优化网络（CON），以提高低能见度环境下的检测可靠性。它还包括一个由动态IoU调整（DIA）改进的深度相似集成（DSI）模块，该模块结合了运动预测和外观线索，以实现可靠的识别关联。该框架使用基于图的轨迹恢复（GBTR）网络和神经轨迹平滑（NTS）来恢复中断的轨迹并确保时间一致性。基于转换器的关联（TBA）模块进一步增强了时态关联。TraceNet在四个具有挑战性的基准测试中取得了出色的表现，mo17和mo20的HOTA得分分别为66.9和66.7，IDF1得分分别为83.2和83.5。这些结果突出了TraceNet在密集和闭塞场景中的鲁棒性，并证明了它是一种高性能和可扩展的实时多目标跟踪解决方案。

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引用次数: 0

Intelligent traffic management with Mamba-ATSP: Optimizing signal control and path planning using multi-modal data fusion 使用Mamba-ATSP的智能交通管理：使用多模态数据融合优化信号控制和路径规划

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-02-01 DOI: 10.1016/j.aej.2026.01.010

Xue Li , Zheng Li , Yan Wang

Efficient processing of multi-source data and dynamic environments is essential for addressing the complex multi-task challenges in intelligent traffic signal control and path planning. Traditional models cannot capture time dependence, global information, and non-linearity relationships inherent in traffic flows. Optimization performance is limited. for dealing with these challenges, we introduce Mamba-ATSP, self-attention mechanism and manaba framework are integrated. Mamba-ATSP In spatial time management, traffic signal optimization and route planning are characterized by modules that integrate multi modal data mission planning and ego attention. Experimental results demonstrate that Mamba-ATSP outperforms established models, such as DeepTraffic, ST-Mamba, and LightPath, on the PEMS and HighD datasets, achieving superior accuracy and efficiency across key metrics like traffic flow, path selection time, average waiting time, and real-time responsiveness. Ablation studies underscore the significance of each module, emphasizing the crucial roles of multi-modal fusion, self-attention, and the Mamba framework. Mamba-ATSP not only does it improve traffic signal control and operation planning, but also shows strong adaptability in dynamic traffic environments and extends its potential to other intelligent traffic systems.

有效地处理多源数据和动态环境是解决智能交通信号控制和路径规划中复杂的多任务挑战的关键。传统模型无法捕捉交通流的时间依赖性、全局信息和非线性关系。优化性能是有限的。为了应对这些挑战，我们引入了Mamba-ATSP，将自注意机制与manaba框架相结合。在空间时间管理中，交通信号优化和路径规划的特点是集成了多模态数据、任务规划和自我关注的模块。实验结果表明，Mamba-ATSP在PEMS和HighD数据集上优于DeepTraffic、ST-Mamba和LightPath等已建立的模型，在交通流量、路径选择时间、平均等待时间和实时响应等关键指标上实现了卓越的准确性和效率。消融研究强调了每个模块的重要性，强调了多模态融合、自我关注和曼巴框架的关键作用。Mamba-ATSP不仅改善了交通信号控制和运营规划，而且在动态交通环境中表现出较强的适应性，并将其潜力扩展到其他智能交通系统。

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引用次数: 0

Edge-refined U-net for multi-task weed detection and segmentation with pixel-level boundary refinement 基于像素级边界细化的多任务杂草检测和分割边缘细化U-net

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-02-01 DOI: 10.1016/j.aej.2026.01.030

Amani K. Samha , Eatedal Alabdulkreem , Mohammed Aljaafari , Hany Mahgoub , Asma A. Alhashmi , Yaser Abdulaziz Alsahafi , Tawfiq Hasanin , Monir Abdullah

Weed detection and segmentation, particularly in the presence of multiple weed classes, present critical challenges for spraying robots in crop fields. Traditional weed classification models are limited to categorizing individual weeds, such as grasses or broadleaf weeds. Existing deep learning techniques have been proposed for selective fields or field-dependent models that use rough outlines with YOLO annotations, which often lack clear boundaries. These result in errors at the edges, where precision is most critical. To overcome these challenges, this research proposed a multi-class weed-detection and segmentation model that can identify all weed types across any field. The practical and scalable nature of this model makes it a unique contribution and a real-time solution for complex weed fields. The present study proposes a novel semantic segmentation framework utilizing an edge-refined enhanced U-Net and a deep learning model for pixel-level analysis of weeds and crops. The input data is pre-processed by extracting vegetation regions using an Excess Green (ExG) and Otsu thresholding approach, effectively suppressing irrelevant soil background. The edge-refined U–Net–based model is trained for three-class segmentation as multi-subclass weed segmentation, crop, and background, which is enhanced with auxiliary edge supervision to improve boundary refinement. This proposed model is a multitask model trained and developed on the complex 16-class weed dataset (MH-WEED16) from Intel RealSense field imagery. The model is further tested on an unseen Sorghum weed dataset and real-time agricultural images. Experimental results show that the proposed model achieves robust performance across multiple evaluation metrics like Dice score of 80 %, IoU of 70 %, boundary IoU of 72 %, and Hausdorff distance as 39 for automated weed–crop discrimination. This pipeline offers a practical, scalable approach to advancing precision agriculture by enhancing the semantic understanding of crop fields.

杂草检测和分割，特别是在多种杂草存在的情况下，对农田喷洒机器人提出了严峻的挑战。传统的杂草分类模型仅限于分类单个杂草，如禾本科或阔叶杂草。现有的深度学习技术已经提出用于选择性领域或领域相关模型，这些模型使用带有YOLO注释的粗略轮廓，通常缺乏明确的边界。这导致在边缘处出现误差，而精度是最关键的。为了克服这些挑战，本研究提出了一个多类别杂草检测和分割模型，该模型可以识别任何领域的所有杂草类型。该模型的实用性和可扩展性使其成为复杂杂草田的独特贡献和实时解决方案。本研究提出了一种新的语义分割框架，利用边缘改进的U-Net和深度学习模型对杂草和作物进行像素级分析。输入数据通过使用过量绿色（ExG）和Otsu阈值方法提取植被区域进行预处理，有效抑制不相关的土壤背景。将基于u - net的边缘细化模型训练为多子类杂草分割、作物分割和背景分割三大类，并通过辅助边缘监督进行增强，以提高边界的精细化程度。该模型是在英特尔RealSense现场图像的复杂16类杂草数据集（MH-WEED16）上训练和开发的多任务模型。该模型在一个未知的高粱杂草数据集和实时农业图像上进一步测试。实验结果表明，该模型在多个评价指标上具有鲁棒性，如Dice得分为80 %，IoU为70 %，边界IoU为72 %，Hausdorff距离为39。该管道提供了一种实用的、可扩展的方法，通过增强对农田的语义理解来推进精准农业。

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引用次数: 0

Beyond pixels: Zero-shot classification of chest X-ray reports via prompt engineering and LLMs 超越像素：通过即时工程和llm对胸部x射线报告进行零射击分类

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-02-01 DOI: 10.1016/j.aej.2026.01.022

Hassan Katal, Jamal Ghasemi, Meysam Roostaee

Accurately extracting structured clinical labels from unstructured chest radiology reports, particularly the MIMIC-CXR dataset, is critical for robust Clinical Decision Support Systems. However, conventional supervised deep learning faces major challenges: reliance on expert-annotated datasets, which are costly and time-consuming to curate, and the computational overhead of training or fine-tuning complex models. To circumvent these limitations, this study proposes a Prompt based Zero Shot Learning Framework that leverages the semantic reasoning capabilities of pre trained LLM to classify radiology reports without requiring gradient-based training or labeled data. We systematically investigate the impact of various text preprocessing pipelines and prompt engineering strategies, including task specific prompts, Persona based instructions, Chain of Thought reasoning, and Ensemble Prompting, alongside mechanisms for managing label uncertainty. Experimental results demonstrate that the optimal configuration, utilizing the IMPRESSION section of reports with specific uncertainty handling, achieves a weighted average F1 score of 0.85 and an Area Under the Curve of 0.92. These results significantly outperform traditional rule based labelers and unsupervised baselines in the domain of clinical text classification. This study substantiates the potential of NLP and prompt engineering to automate medical report structuring, offering a scalable and label free alternative to traditional supervised learning for healthcare applications.

准确地从非结构化的胸部放射学报告中提取结构化的临床标签，特别是MIMIC-CXR数据集，对于健全的临床决策支持系统至关重要。然而，传统的监督深度学习面临着主要挑战：依赖专家注释的数据集，这是昂贵和耗时的，以及训练或微调复杂模型的计算开销。为了规避这些限制，本研究提出了一个基于提示的零射击学习框架，该框架利用预先训练的LLM的语义推理能力对放射学报告进行分类，而不需要基于梯度的训练或标记数据。我们系统地研究了各种文本预处理管道和提示工程策略的影响，包括任务特定提示、基于角色的指令、思维链推理和集成提示，以及管理标签不确定性的机制。实验结果表明，利用具有特定不确定度处理的报告的印象部分，最优配置的F1加权平均得分为0.85，曲线下面积为0.92。这些结果显著优于传统的基于规则的标注器和临床文本分类领域的无监督基线。这项研究证实了NLP和提示工程自动化医疗报告结构的潜力，为医疗保健应用提供了传统监督学习的可扩展和无标签替代方案。

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引用次数: 0

Efficient entropy estimation for inverted exponentiated Pareto distribution using ranked set sampling: A comparative study 利用秩集抽样的倒指数Pareto分布的有效熵估计：比较研究

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-23 DOI: 10.1016/j.aej.2026.01.018

Amal S. Hassan , Tmader Alballa , Etaf Alshawarbeh , Rehab Alsultan , Said G. Nassr , Rokaya Elmorsy Mohamed

Entropy, a key concept in information theory, measures the degree of unpredictability or uncertainty present in a random variable or system. It plays a vital role across various disciplines, including communication theory, thermodynamics, and statistical mechanics. On the other hand, Ranked Set Sampling (RSS) provides an effective approach to mitigating the challenges associated with costly or complex measurement procedures. Given the wide-ranging applications of the inverted exponentiated Pareto distribution, this study investigates the estimation of its parameters and various entropy measures, encompassing Havrda and Charvát, Tsallis, Rényi, and Arimoto. We examine the performance of these estimators under both RSS and simple random sampling (SRS) frameworks.To tackle this task, seven classical estimation techniques are employed: maximum product spacing, least squares, Kolmogorov, Anderson-Darling, weighted least squares, maximum likelihood, and Cramér-von Mises. Using an equal number of measured units, simulation studies evaluates the performance of estimators derived from SRS and RSS, considering both perfect and imperfect ranking scenarios. Three evaluation criteria are adopted for comparison: relative efficiency, mean squared error, and absolute bias. In assessing the estimated quality of RSS and SRS, the Kolmogorov technique appears beneficial in most cases, based on numerical results. In terms of estimation accuracy, RSS consistently performs better than SRS, regardless of whether the ranking is perfect or imperfect. Additionally, compared to imperfect ranking method, perfect ranking produces estimates that are more accurate. The advantage of the RSS design over the SRS design is further supported by real data results that indicate the tensile strength measures in GPA carbon fibers.

熵是信息论中的一个关键概念，用来衡量随机变量或系统中不可预测性或不确定性的程度。它在各个学科中起着至关重要的作用，包括通信理论、热力学和统计力学。另一方面，排序集抽样（RSS）提供了一种有效的方法来减轻与昂贵或复杂的测量过程相关的挑战。鉴于倒指数帕累托分布的广泛应用，本研究调查了其参数和各种熵测度的估计，包括Havrda和Charvát， Tsallis， rsamunyi和Arimoto。我们研究了这些估计器在RSS和简单随机抽样（SRS）框架下的性能。为了完成这项任务，采用了七种经典的估计技术：最大产品间距、最小二乘、Kolmogorov、Anderson-Darling、加权最小二乘、最大似然和cramsamr -von Mises。使用相同数量的测量单元，模拟研究评估了从SRS和RSS派生的估计器的性能，考虑了完美和不完美的排序场景。采用相对效率、均方误差和绝对偏差三个评价标准进行比较。在评估RSS和SRS的估计质量时，基于数值结果，Kolmogorov技术在大多数情况下似乎是有益的。就估计准确性而言，无论排名是完美的还是不完美的，RSS始终比SRS表现得更好。此外，与不完全排序法相比，完全排序法产生的估计更准确。实际数据结果表明，GPA碳纤维的抗拉强度指标进一步支持了RSS设计优于SRS设计。

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引用次数: 0

From policy frameworks to AI mentoring practice: A structured approach to responsible innovation in architectural education 从政策框架到人工智能指导实践：建筑教育中负责任创新的结构化方法

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-21 DOI: 10.1016/j.aej.2026.01.012

Hosam Salah El Samaty , Noorh Albadi

Addressing the policy-to-practice gap in AI-supported architectural education, this study examines a structured “AI Mentoring Method” in alignment with King Abdulaziz University’s AI policy framework. Implemented in a senior undergraduate architectural research course across two consecutive semesters under different instructors, the method is organized into three stages: design, application, and evaluation, and systematically integrated across five course chapters. Generative AI tools were embedded through instructor-mediated tasks grounded in guided inquiry, scaffolding, and reflective practice. The study adopts an explanatory case study approach combining student satisfaction surveys, longitudinal quantitative assessment of Intended Learning Outcomes, and qualitative evidence from student work samples. Survey data were analyzed descriptively, while learning outcomes were compared across three semesters (pre-implementation and post-implementation under two instructors). Results indicate improved AI literacy, sustained learning gains, and strengthened value-based outcomes related to ethical awareness and academic responsibility. Variations between cohorts highlight the critical role of the instructor in shaping AI-supported learning, despite applying the same methodological framework. The study contributes a pedagogically grounded and policy-aligned model for responsible AI integration. While limited by a single-course context, the findings suggest that the AI Mentoring Method offers a transferable framework for structured, instructor-led AI adoption in design and research-based curricula.

为了解决人工智能支持的建筑教育中政策与实践之间的差距，本研究根据阿卜杜勒阿齐兹国王大学的人工智能政策框架，研究了一种结构化的“人工智能指导方法”。该方法在一个连续两个学期的本科建筑研究课程中实施，由不同的导师指导，分为三个阶段：设计、应用和评估，并系统地整合在五个课程章节中。生成式人工智能工具通过基于指导性探究、脚手架和反思性实践的教师中介任务嵌入。本研究采用解释性案例研究方法，结合学生满意度调查、预期学习成果的纵向定量评估和学生作业样本的定性证据。对调查数据进行描述性分析，同时对三个学期的学习成果进行比较（在两名教师的指导下实施前和实施后）。结果表明，人工智能素养得到提高，持续的学习成果，以及与道德意识和学术责任相关的基于价值的成果得到加强。尽管采用相同的方法框架，但不同群组之间的差异突出了教师在塑造人工智能支持学习方面的关键作用。该研究为负责任的人工智能集成提供了一个以教学为基础和与政策一致的模型。虽然受到单一课程背景的限制，但研究结果表明，人工智能指导方法为在设计和研究型课程中采用结构化、教师主导的人工智能提供了一个可转移的框架。

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引用次数: 0

Fiber reinforced cementitious matrix bond behavior on masonry substrate 纤维增强水泥基在砌体基板上的粘结性能

IF 6.8 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

alexandria engineering journal

Pub Date : 2026-01-21 DOI: 10.1016/j.aej.2026.01.008

Jin Lu , Xiaofan Liao

In this study, the Multi Pier MP method, previously presented for Fiber Reinforced Polymer (FRP) delamination on masonry substrates, is adapted for the Fiber Reinforced Cementitious Matrix (FRCM) single lap shear behavior. Based on this method, an assemblage of vertical (piers) and diagonal (braces) truss members forms a two-dimensional truss structure, representing the entire composite system, including fibers, matrix, and masonry substrate. Piers and braces carry the normal and shear stresses of the system, respectively. With the help of the materials' axial stress-strain curve, the non-linear behavior of truss members was identified. All possible failure modes, such as rupture, fiber slippage, and matrix shear damage, were considered. Validation on four experimental FRCM reinforced masonry pillars demonstrated that the model can estimate the composite's single lap shear behavior and their failure mechanisms with acceptable accuracy. Additionally, all internal stresses and displacement profiles were obtained across the reinforcements for both elastic and plastic ranges up to failure. Despite its accuracy and comprehensiveness, minimal time was required to implement this method in any commercial code, only needing to address non-linearity in unidirectional elements.

在本研究中，先前提出的用于砌体基板上纤维增强聚合物（FRP）分层的多墩MP方法适用于纤维增强胶凝基质（FRCM）单搭接剪切行为。基于这种方法，垂直（墩）和对角（撑）桁架构件的组合形成了一个二维桁架结构，代表了整个复合系统，包括纤维、基质和砌体基底。桥墩和支撑分别承受体系的法向应力和剪应力。借助材料轴向应力-应变曲线，识别了桁架构件的非线性行为。考虑了所有可能的破坏模式，如断裂、纤维滑移和基体剪切损伤。对4根frp筋砌体柱的试验验证表明，该模型能较好地预测复合材料的单搭剪性能及其破坏机制。此外，所有的内部应力和位移曲线都得到了在弹性和塑性范围内的钢筋，直到破坏。尽管它的准确性和全面性，在任何商业代码中实现这种方法所需的时间最少，只需要处理单向元素的非线性。

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引用次数: 0