The real-time compaction quality evaluation of earth-rock dam plays a pivotal role in ensuring dam safety. However, the current real-time compaction quality evaluation only takes the physical properties of compacted dam materials into account, which fails to characterize whether their mechanical property meets the requirements of deformation and destruction, and no quantitative heterogeneity of real-time compaction quality is studied. This paper presents a comprehensive evaluation method to address these problems. First, based on on-site tests, real-time physical and mechanical indices are obtained. Next, the analytic hierarchy process, extended by the interval model (i-AHP) method, is introduced for real-time compaction quality evaluation considering both these indices, and the hybrid compaction index (HCI) is firstly proposed based on the i-AHP method. Finally, an improved geostatistical analysis method (i-GAM) is developed to quantify the real-time compaction quality heterogeneity. A case study of an earth-rock dam project in southwest China demonstrates the effectiveness and advantages of the proposed method.
Machine learning is widely used for predicting the compressive strength of concrete. However, the machine learning modeling process relies on expert experience. Automated machine learning (AutoML) aims to automatically select optimal data preprocessing methods, feature preprocessing methods, machine learning algorithms, and hyperparameters according to the datasets used, to obtain high-precision prediction models. However, the effectiveness of modeling concrete compressive strength using AutoML has not been verified. This study attempts to fill the above research gap. We construct a database comprising four different types of concrete datasets and compare one AutoML algorithm (Auto-Sklearn) against five ML algorithms. The results show that Auto-Sklearn can automatically build an accurate concrete compressive strength prediction model without relying on expert experience. In addition, Auto-Sklearn achieves the highest accuracy for all four datasets, with an average R2 of 0.953; the average R2 values of the ML models with tuned hyperparameters range from 0.909 to 0.943. This study verifies for the first time the feasibility of AutoML for concrete compressive strength prediction, to allow concrete engineers to easily build accurate concrete compressive strength prediction models without relying on a large amount of ML modeling experience.
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