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Jinlong Liu

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8 papers

2 author rows

EAAI Journal 2026 Journal Article

A metaheuristic-driven categorical boosting framework with interpretability for high-precision prediction of mechanical properties in corroded reinforced concrete beams

Yuzhuo Zhang
Zheng Wang
Jinlong Liu
Yalin Li
Zhenqin Huang
Xiaohu Yu

The degradation of mechanical properties in corroded reinforced concrete (RC) beams presents a major challenge for assessing structural durability. To address this issue, this study proposes an integrated machine learning (ML) framework to predict the mechanical properties of such beams. First, a database of 464 samples was established, including 12 input parameters and 2 output parameters, followed by correlation analysis of the inputs. On this basis, the applicability of existing design codes and empirical models was evaluated. Subsequently, eight ML models were trained, with their hyperparameters optimized via Bayesian optimization (BO) to enhance prediction accuracy. The Categorical Boosting (CatBoost) model was identified as the most accurate, and its hyperparameters were further optimized using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for improved performance. Results show the PSO-optimized CatBoost model achieves the highest prediction accuracy to date: for the flexural strength test set, the coefficient of determination (R 2 ) is 0. 984 and root mean square error (RMSE) is 3. 1602; for the deflection test set, R 2 is 0. 975 and RMSE is 0. 6259. Compared with design codes, flexural strength test set R 2 increases by 27. 3 % and RMSE decreases by 72. 8 %; versus traditional models like Support Vector Regression (SVR), R 2 rises by 5. 4 % and RMSE drops by 43. 5 %. Additionally, SHapley Additive exPlanations (SHAP) analysis reveals geometric parameters (beam height, beam width) dominate flexural strength, while elastic stiffness and beam length drive deflection. Finally, a user-friendly graphical user interface (GUI) was developed for rapid mechanical property assessment of corroded RC beams.

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EAAI Journal 2026 Journal Article

Damage assessment of thermal-humidity-mechanical coupling field of early-age concrete based on adaptive physics informed neural network

Shiqi Wang
Yue Chen
Jinlong Liu
Fangzhou Lin
Lei Xu

The crack-damage resistance of early-age concrete is affected by multiple factors such as hydration, self-drying, temperature and humidity diffusion, and material properties, which are difficult to be accurately evaluated by traditional theories and numerical models. This paper proposed an adaptive physics-informed back propagation neural network (BPINN) to accurately evaluate the damage of early-age concrete under multi-physics field coupling. The temperature and humidity diffusion and shrinkage models are used as physics loss functions to guide the model in learning the physics laws. Furthermore, time-dependent factor weights are constructed for both the physics and boundary equations to enhance the model's ability to learn the spatiotemporal feature distribution of the sampling points. BPINN effectively simulates the influence of concrete strength grade and boundary conditions on temperature and humidity diffusion, with the average error less than 5 %. The LOSS differences of traditional physics informed neural network (PINN) and BPINN in time step, activation function, hidden layer and neuron number are quantified. Compared with the traditional PINN, the LOSS of BPINN is reduced by 62. 4 %. On this basis, the predictive performance of BPINN and four types of data-driven models is compared to verify the influence of physics constraint, as BPINN has the smallest statistical loss and data discreteness. The model proposed in this paper enhances the learning ability of spatial-temporal features by balancing the weight between boundary and physics equations, providing new insights for the thermo-hygro-mechanical coupling field in early-age concrete.

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EAAI Journal 2026 Journal Article

Engineering graphene and carbon nanotube reinforced cement composites for intelligent pavement as noise-resistant wireless monitoring sensors

Yucheng Fan
Chuang Feng
Luming Shen
Hongru Xiao
Shiqi Wang
Jinlong Liu
Wengui Li

Hybrid graphene nanoplatelet/carbon nanotube reinforced cement-based sensors (GNP/CNTRCS) offer significant advantages for self-sensing pavements in road infrastructure monitoring. However, real-world engineering applications face challenges such as the necessity of wired electrical resistance measurement and the inherent nonlinear and noisy piezoresistive response, which hinder automated data processing. To address these issues, this study develops a four-electrode wireless pavement monitoring system integrating GNP/CNTRCS with machine learning (ML) algorithms for noise-resistant vehicle classification and speed estimation. Laboratory and field tests are conducted to validate the piezoresistive performance and noisy condition of the GNP/CNTRCS, with fractional change in resistivity (FCR) ranging from −40 % for cars to −3 % for pedestrians and 260 sets of time-series data are collected under different vehicle loads and speeds for ML. Innovatively employing continuous wavelet transform (CWT) for noise-resistant feature extraction, the convolutional neural network (CNN) achieves 96. 1 % classification accuracy, maintaining 91. 8 % under noise. Furthermore, a proposed hierarchical regression strategy establishes state-of-the-art (SOTA) performance for speed estimation with an R2 of 0. 851, sustaining 0. 813 under noise. This work provides a scalable and low-maintenance solution for intelligent transportation infrastructure.

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AAAI Conference 2026 Conference Paper

FUSE: Fine-Grained and Semantic-Aware Learning for Unified Image Understanding and Generation

Peng Zhang
Wanggui He
Mushui Liu
Wenyi Xiao
Siyu Zou
Yuan Li
Xingjian Wang
Guanghao Zhang

Recent unified models have demonstrated that the reasoning capacity of Multimodal Large Language Models (MLLMs) can be leveraged to facilitate diffusion-based image generation with impressive flexibility and performance. However, approaches that rely heavily on MLLMs for high-level semantic encoding often struggle with fine-grained visual tasks like image editing and virtual try-on. To address this gap, we propose FUSE, a unified framework excelling at both high-level vision–language understanding and fine-grained generation. First, we introduce a Semantic-to-Detail Connector that pre-aligns fine-grained visual features with the MLLM's semantic space. This design counteracts the low-level information loss inherent in MLLM encodings, creating a unified representation that steers the diffusion process with both global semantics and rich local details. Second, to further enhance semantic awareness and detail preservation, we introduce Adaptive-GRPO, a post-training objective that dynamically balances semantic coherence against pixel-level fidelity. The integration of these two innovations allows FUSE to generate images that are both semantically faithful and visually fine-grained. Comprehensive experiments on text-to-image and instruction-guided editing benchmarks show that FUSE significantly outperforms existing unified baselines, achieving 0.89 on Geneval, 0.65 on WISE, and 3.88 on ImageEdit.

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EAAI Journal 2026 Journal Article

Lightweight Kolmogorov-Arnold Network with dual-objective optimization for axial capacity prediction of square coal gangue concrete-filled steel tube stub columns based on finite element simulation

Xiangyu Kong
Yaowei Fan
Jinlong Liu
Meng Xi
Yuzhuo Zhang
Yang Yu

Square coal gangue concrete-filled steel tube (CGCFST) stub columns offer both environmental benefits from solid waste utilization and enhanced mechanical performance from steel tube confinement. However, accurate prediction of their axial compressive bearing capacity (N u ) remains challenging due to the absence of dedicated design codes and limitations of existing machine learning approaches. Specifically, tree-based models yield piecewise constant predictions incapable of capturing the smooth continuous parameter relationship, while conventional neural networks struggle to balance model compactness with prediction accuracy. To address these gaps, this study proposes a dual-objective optimized Kolmogorov-Arnold Network (KAN) framework that simultaneously minimizes root mean square error (RMSE) and model complexity (edge function count). A parametric database of 1470 samples was generated via ABAQUS batch simulations, covering five key design parameters: cross-section side length (D), steel tube thickness (t), coal gangue replacement ratio (r), concrete compressive strength (f c '), and steel yield strength (f y ). Six existing design codes were systematically evaluated, revealing significant errors when applied to CGCFST. The optimized KAN achieves a coefficient of determination (R 2 ) of 0. 9993, outperforming Multilayer Perceptron (MLP) variants and all other Bayesian-optimized tree models except Extreme Gradient Boosting (XGBoost), while providing inherently continuous predictions. After two pruning iterations, KAN retains high accuracy (R 2 = 0. 9713) with only 2. 8% degradation, confirming its lightweight potential. Sensitivity and parametric analyses quantify the contribution of each design parameter to N u. Finally, a Graphical User Interface (GUI) integrating ABAQUS batch simulation with KAN prediction is developed to facilitate rapid and accurate capacity assessment for engineering applications.

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AAAI Conference 2025 Conference Paper

Biased Incomplete Multi-View Learning

Haishun Chen
Cai Xu
Ziyu Guan
Wei Zhao
Jinlong Liu

Considering the ubiquitous phenomenon of missing views in multi-view data, incomplete multi-view learning is a crucial task in many applications. Existing methods usually follow an impute-then-predict strategy for handling this problem. However, they often assume that the view-missing patterns are uniformly random in multi-view data, which does not agree with real-world scenarios. In practice, view-missing patterns often vary across different classes. For example, in the medical field, patients with rare diseases would take more examinations than those with common diseases; in the financial field, high-risk customers tend to receive evaluations from more views than ordinary ones. Hence, we often observe that data-rich classes suffer limited views while data-poor classes suffer limited samples. Previous methods would typically fail due to such biased view-missing patterns. This motivates us to delve into a new biased incomplete multi-view learning problem. To this end, we develop a Reliable Incomplete Multi-view Learning (RIML) method. RIML is a simple yet effective learning-free imputation framework that goes beyond the conventional approaches by considering information from all classes, rather than just relying on individual views or within-class samples. Specifically, we utilize an inter-class association matrix that allows data-poor classes to refer the knowledge from data-rich classes. This enables the construction of more reliable view-specific distributions, from which we perform multiple samplings to recover missing views. Additionally, to obtain a reliable multi-view representation for downstream tasks, we develop an enhanced focal loss with a category-aware marginal term to learn a more distinguishable feature space. Experiments on five multi-view datasets demonstrate that RIML significantly outperforms existing methods in both accuracy and robustness.

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AAAI Conference 2025 Conference Paper

Resolving Multi-Condition Confusion for Finetuning-Free Personalized Image Generation

Qihan Huang
Siming Fu
Jinlong Liu
Hao Jiang
Yipeng Yu
Jie Song

Personalized text-to-image generation methods can generate customized images based on the reference images, which have garnered wide research interest. Recent methods propose a finetuning-free approach with a decoupled cross-attention mechanism to generate personalized images requiring no test-time finetuning. However, when multiple reference images are provided, the current decoupled cross-attention mechanism encounters the object confusion problem and fails to map each reference image to its corresponding object, thereby seriously limiting its scope of application. To address the object confusion problem, in this work we investigate the relevance of different positions of the latent image features to the target object in diffusion model, and accordingly propose a weighted-merge method to merge multiple reference image features into the corresponding objects. Next, we integrate this weighted-merge method into existing pre-trained models and continue to train the model on a multi-object dataset constructed from the open-sourced SA-1B dataset. To mitigate object confusion and reduce training costs, we propose an object quality score to estimate the image quality for the selection of high-quality training samples. Furthermore, our weighted-merge training framework can be employed on single-object generation when a single object has multiple reference images. The experiments verify that our method achieves superior performance to the state-of-the-arts on the Concept101 dataset and DreamBooth dataset of multi-object personalized image generation, and remarkably improves the performance on single-object personalized image generation.

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ICLR Conference 2020 Conference Paper

Understanding Why Neural Networks Generalize Well Through GSNR of Parameters

Jinlong Liu
Yunzhi Bai
Guoqing Jiang
Ting Chen
Huayan Wang

As deep neural networks (DNNs) achieve tremendous success across many application domains, researchers tried to explore in many aspects on why they generalize well. In this paper, we provide a novel perspective on these issues using the gradient signal to noise ratio (GSNR) of parameters during training process of DNNs. The GSNR of a parameter is simply defined as the ratio between its gradient's squared mean and variance, over the data distribution. Based on several approximations, we establish a quantitative relationship between model parameters' GSNR and the generalization gap. This relationship indicates that larger GSNR during training process leads to better generalization performance. Futher, we show that, different from that of shallow models (e.g. logistic regression, support vector machines), the gradient descent optimization dynamics of DNNs naturally produces large GSNR during training, which is probably the key to DNNs’ remarkable generalization ability.

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