As conventional machine learning models often struggle with scarcity and structural variation of training data, this paper proposes a novel regression transfer learning framework called transferable tabular regressor (TransTabRegressor) to address this challenge. The TransTabRegressor integrates natural language processing (NLP) for feature encoding, transformer for enhanced feature representation, and deep learning (DL) for robust modeling, facilitating effective transfer learning across tabular datasets using reducing input parameters. By leveraging the NLP data processor, the framework embeds both parameter names and values, enabling it to recognize and adapt to different expressions of similar parameters. For instance, the bond strength of fiber‐reinforced polymer (FRP) bars embedded in ultra‐high‐performance concrete (UHPC) is critical for ensuring the integrity of FRP‐UHPC structures. While pullout tests are widely adopted for their simplicity to generate substantial data, beam tests provide a closer approximation to actual stress conditions but are more complex thus resulting in limited data size. As a verification, the framework is applied to predict the bond strength of FRP bars embedded in UHPC using limited beam test data. A pre‐trained model is first established using 479 pieces of pullout test data. Subsequently, two transfer learning models are developed by fine‐tuning on 115 pieces of beam test data, where 66 correspond to concrete splitting failure and 49 correspond to pullout failure. For comparative analysis, XGBoost and neural network models are directly trained on the beam test data. Evaluation results demonstrate that the transfer learning models achieve significantly improved prediction accuracy and generalization capability. This study significantly highlights the effectiveness of the proposed TransTabRegressor in handling data scarcity and variability in input parameters across various engineering applications.

中文翻译：

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跨不同表格数据集的基于自然语言处理的深度迁移学习模型，用于混凝土中复合筋的粘结强度预测


由于传统的机器学习模型经常与训练数据的稀缺性和结构变化作斗争，本文提出了一种新的回归迁移学习框架，称为可转移表格回归器 （TransTabRegressor） 来应对这一挑战。TransTabRegressor 集成了用于特征编码的自然语言处理 （NLP）、用于增强特征表示的转换器和用于稳健建模的深度学习 （DL），从而使用减少输入参数促进跨表格数据集的有效迁移学习。通过利用 NLP 数据处理器，该框架嵌入了参数名称和值，使其能够识别和适应相似参数的不同表达式。例如，嵌入超高性能混凝土 （UHPC） 中的纤维增强聚合物 （FRP） 棒的粘合强度对于确保 FRP-UHPC 结构的完整性至关重要。虽然拉拔测试因其生成大量数据的简单性而被广泛采用，但梁测试提供更接近实际应力条件，但更复杂，因此导致数据量有限。作为验证，该框架用于使用有限光束测试数据预测嵌入 UHPC 中的 FRP 棒的粘合强度。首先使用 479 条拉出测试数据建立预训练模型。随后，通过对 115 条梁测试数据进行微调，开发了两个迁移学习模型，其中 66 个对应于混凝土劈裂破坏，49 个对应于拉拔破坏。为了进行比较分析，XGBoost 和神经网络模型直接在光束测试数据上进行训练。评估结果表明，迁移学习模型实现了显著提高的预测准确性和泛化能力。 这项研究显着强调了所提出的 TransTabRegressor 在处理各种工程应用程序中输入参数的数据稀缺性和可变性方面的有效性。