TUM bond database with full-scale beam tests on anchorages and laps in tension

Civil Engineering Design Pub Date : 2026-03-19 Epub Date: 2026-01-07 DOI:10.1002/cend.70025
Johannes Fröse, Leopold Staller, Oliver Fischer
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Abstract

Reliable experimental data is essential for the development and validation of structural design models. However, existing bond databases often lack consistency, traceability, and overall data quality. To address this, the Technical University of Munich (TUM) developed the TUM Bond Database (TUMBD), a curated collection of experimental test data focusing on lap splices and anchorages under tensile loading. TUMBD includes a variety of full-scale beam tests from the primary literature and tests conducted at TUM. The TUMBD currently comprises 1033 lap splice and 189 anchorage tests, with detailed information on geometry, test setup, materials, and experimental results. This paper discusses the database's origin and development, its structure and content, and presents a statistical analysis of the distribution of key parameters. The open-access database supports model calibration and data-driven research, including the use of artificial intelligence and advanced computational methods. Given the complexity of bond behavior, the emergence of new resource-efficient materials, and the potential of such advanced statistical techniques, TUMBD enhances the reliability, sustainability, and efficiency of reinforced concrete construction. Ongoing updates and strict criteria ensure data quality.

Abstract Image

TUM粘结数据库与全尺寸梁测试的锚固和圈在张力
可靠的实验数据对结构设计模型的建立和验证至关重要。然而,现有的债券数据库通常缺乏一致性、可追溯性和整体数据质量。为了解决这个问题,慕尼黑工业大学(TUM)开发了TUM Bond数据库(TUMBD),这是一个收集实验测试数据的集合,重点关注拉伸载荷下的搭接和锚固。TUMBD包括来自主要文献的各种全尺寸光束测试和在TUM进行的测试。TUMBD目前包括1033个搭接和189个锚固测试,并提供了几何形状、测试设置、材料和实验结果的详细信息。本文论述了该数据库的起源、发展、结构和内容,并对关键参数的分布进行了统计分析。开放访问数据库支持模型校准和数据驱动研究,包括使用人工智能和先进的计算方法。考虑到粘结行为的复杂性,新型资源高效材料的出现,以及这种先进统计技术的潜力,TUMBD增强了钢筋混凝土结构的可靠性、可持续性和效率。持续更新和严格的标准确保数据质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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