Perry Marcus, Fusiek Grzegorz, Niewczas Pawel, Rubert Tim, McAlorum Jack
Department of Civil & Environmental Engineering, University of Strathclyde, Glasgow G1 1XJ, UK.
Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK.
Sensors (Basel). 2017 Dec 16;17(12):2928. doi: 10.3390/s17122928.
Wind turbine foundations are typically cast in place, leaving the concrete to mature under environmental conditions that vary in time and space. As a result, there is uncertainty around the concrete's initial performance, and this can encourage both costly over-design and inaccurate prognoses of structural health. Here, we demonstrate the field application of a dense, wireless thermocouple network to monitor the strength development of an onshore, reinforced-concrete wind turbine foundation. Up-to-date methods in fly ash concrete strength and maturity modelling are used to estimate the distribution and evolution of foundation strength over 29 days of curing. Strength estimates are verified by core samples, extracted from the foundation base. In addition, an artificial neural network, trained using temperature data, is exploited to demonstrate that distributed concrete strengths can be estimated for foundations using only sparse thermocouple data. Our techniques provide a practical alternative to computational models, and could assist site operators in making more informed decisions about foundation design, construction, operation and maintenance.
风力涡轮机基础通常是现浇的，混凝土在随时间和空间变化的环境条件下成熟。因此，混凝土的初始性能存在不确定性，这可能导致成本高昂的过度设计以及对结构健康状况的不准确预测。在此，我们展示了一个密集的无线热电偶网络在现场的应用，以监测陆上钢筋混凝土风力涡轮机基础的强度发展。采用粉煤灰混凝土强度和成熟度建模的最新方法来估计养护29天期间基础强度的分布和演变。通过从基础底部提取的芯样对强度估计进行验证。此外，利用一个使用温度数据训练的人工神经网络来证明，仅使用稀疏的热电偶数据就可以估计基础的分布式混凝土强度。我们的技术为计算模型提供了一种实用的替代方法，并可以帮助现场操作人员在基础设计、施工、运营和维护方面做出更明智的决策。
