Report of RILEM TC 281-CCC: insights into factors affecting the carbonation rate of concrete with SCMs revealed from data mining and machine learning approaches

IF 3.4 3区 工程技术 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
A. Vollpracht, G. J. G. Gluth, B. Rogiers, I. D. Uwanuakwa, Q. T. Phung, Y. Villagran Zaccardi, C. Thiel, H. Vanoutrive, J. M. Etcheverry, E. Gruyaert, S. Kamali-Bernard, A. Kanellopoulos, Z. Zhao, I. M. Martins, S. Rathnarajan, N. De Belie
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Abstract

The RILEM TC 281–CCC ‘‘Carbonation of concrete with supplementary cementitious materials’’ conducted a study on the effects of supplementary cementitious materials (SCMs) on the carbonation rate of blended cement concretes and mortars. In this context, a comprehensive database has been established, consisting of 1044 concrete and mortar mixes with their associated carbonation depth data over time. The dataset comprises mix designs with a large variety of binders with up to 94% SCMs, collected from the literature as well as unpublished testing reports. The data includes chemical composition and physical properties of the raw materials, mix-designs, compressive strengths, curing and carbonation testing conditions. Natural carbonation was recorded for several years in many cases with both indoor and outdoor results. The database has been analysed to investigate the effects of binder composition and mix design, curing and preconditioning, and relative humidity on the carbonation rate. Furthermore, the accuracy of accelerated carbonation testing as well as possible correlations between compressive strength and carbonation resistance were evaluated. One approach to summerise the physical and chemical resistance in one parameter is the ratio of water content to content of carbonatable CaO (w/CaOreactive ratio). The analysis revealed that the w/CaOreactive ratio is a decisive factor for carbonation resistance, while curing and exposure conditions also influence carbonation. Under natural exposure conditions, the carbonation data exhibit significant variations. Nevertheless, probabilistic inference suggests that both accelerated and natural carbonation processes follow a square-root-of-time behavior, though accelerated and natural carbonation cannot be converted into each other without corrections. Additionally, a machine learning technique was employed to assess the influence of parameters governing the carbonation progress in concretes.

RILEM TC 281-CCC 报告:通过数据挖掘和机器学习方法深入了解影响含 SCM 混凝土碳化率的因素
RILEM TC 281-CCC "使用辅助胶凝材料的混凝土碳化 "研究了辅助胶凝材料(SCM)对掺合水泥混凝土和砂浆碳化率的影响。在此背景下,建立了一个综合数据库,其中包括 1044 种混凝土和砂浆混合料及其随时间变化的相关碳化深度数据。该数据集包括从文献和未发表的测试报告中收集到的各种粘结剂的混合设计,其中单体水泥含量高达 94%。数据包括原材料的化学成分和物理特性、混合设计、抗压强度、固化和碳化测试条件。在许多情况下,室内和室外的自然碳化结果都有数年的记录。对数据库进行了分析,以研究粘结剂成分和混合设计、固化和预处理以及相对湿度对碳化率的影响。此外,还评估了加速碳化测试的准确性以及抗压强度和抗碳化能力之间可能存在的相关性。将物理抗性和化学抗性归纳为一个参数的方法是含水量与可碳化 CaO 含量之比(w/CaO 反应比)。分析表明,w/CaOreactive 比率是抗碳化性的决定性因素,而固化和暴露条件也会影响碳化。在自然曝晒条件下,碳化数据表现出明显的差异。不过,概率推断表明,加速碳化和自然碳化过程都遵循时间平方根行为,但加速碳化和自然碳化不能相互转换而不进行修正。此外,还采用了机器学习技术来评估混凝土碳化过程参数的影响。
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来源期刊
Materials and Structures
Materials and Structures 工程技术-材料科学:综合
CiteScore
6.40
自引率
7.90%
发文量
222
审稿时长
5.9 months
期刊介绍: Materials and Structures, the flagship publication of the International Union of Laboratories and Experts in Construction Materials, Systems and Structures (RILEM), provides a unique international and interdisciplinary forum for new research findings on the performance of construction materials. A leader in cutting-edge research, the journal is dedicated to the publication of high quality papers examining the fundamental properties of building materials, their characterization and processing techniques, modeling, standardization of test methods, and the application of research results in building and civil engineering. Materials and Structures also publishes comprehensive reports prepared by the RILEM’s technical committees.
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