基于温度-时间历史,使用双参数成熟度ANN模型预测强度的新方法.
1Faculty of Civil Engineering and Architecture, Kielce University of Technology, Al. Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland.
Materials (Basel, Switzerland)
|January 8, 2025
概括
这项研究引入了一个新的神经网络模型来预测混凝土的强度发展,克服了传统的相当成熟时间方法的局限性. 人工神经网络 (ANN) 通过考虑水化热,激活能量,温度和时间来准确估计压力强度.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算机建模 计算建模
背景情况:
- 相当成熟时间 (te) 方法被广泛用于预测在不同温度下混凝土强度的发展.
- 这种方法依赖于激活能量 (Ea),这是一个参数,其与水泥类型,水/水泥比率,温度和添加剂的变化使准确的强度预测变得复杂.
- 确定适当的Ea值在具体科学中仍然是一个持续的挑战.
研究的目的:
- 使用人工神经网络 (ANN) 分析开发一种新的强度-温度历史模型.
- 为了研究水化热 (Q),激活能量 (Ea),温度 (T) 和时间 (t) 对混凝土强度预测的影响.
- 评估ANN模型在估计相对压力强度发展中的准确性.
主要方法:
- 开发一个具有四个输入参数的ANN模型:水化热 (Q),激活能量 (Ea),温度 (T) 和时间 (t).
- 使用六种不同的水泥制成的迫击炮进行实验调查.
- 在5至35°C的温度下固化标本,并在90天内监测强度的发展.
主要成果:
- 该ANN分析方法在估计相对压力强度方面表现出足够的准确性.
- 发现水热 (Q) 显著影响了早期的力量增长.
- 激活能量 (Ea) 被确定为影响后期力量发展的关键因素.
结论:
- 拟议的ANN模型提供了一种可靠的方法,可以根据固化过程中的温度变化来预测混凝土的强度.
- 该模型有效地捕捉了固化条件和力量发展之间的复杂关系.
- 这项研究为优化混凝土施工实践提供了宝贵的工具,可以通过准确预测强度增加来优化混凝土施工实践.
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