This study aims to predict the compressive strength of concrete using a machine-learning algorithm with linear regression analysis and to evaluate its accuracy. The open-source software library TensorFlow was used to develop the machine-learning algorithm. In the machine-earning algorithm, a total of seven variables were set: water, cement, fly ash, blast furnace slag, sand, coarse aggregate, and coarse aggregate size. A total of 4297 concrete mixtures with measured compressive strengths were employed to train and testing the machine-learning algorithm. Of these, 70% were used for training, and 30% were utilized for verification. For verification, the research was conducted by classifying the mixtures into three cases: the case where the machine-learning algorithm was trained using all the data (Case-1), the case where the machine-learning algorithm was trained while maintaining the same number of training dataset for each strength range (Case-2), and the case where the machine-learning algorithm was trained after making the subcase of each strength range (Case-3). The results indicated that the error percentages of Case-1 and Case-2 did not differ significantly. The error percentage of Case-3 was far smaller than those of Case-1 and Case-2. Therefore, it was concluded that the range of training dataset of the concrete compressive strength is as important as the amount of training dataset for accurately predicting the concrete compressive strength using the machine-learning algorithm.
This study investigated the structural safety of the developed panel‐to‐base joint technique for full‐size precast lightweight aggregate concrete (PLC) insulation panels subjected to cyclic lateral loads. The main parameters were the compressive strength of insulating concrete and the safety factor of the joint reinforcing bar for connecting with the base or beam. The test results revealed that the PLC insulation panel specimens with a safety factor of 1.0 experienced severe pinching effect and low ductile performance. However, the PLC insulation panel specimens with a safety factor of 1.6 exhibited higher flexural moment capacity, displacement ductility ratio, and work damage index than those with a safety factor of 1.0. Consequently, the overall flexural behavior of the PLC insulation panel specimens with a safety factor of 1.6 can be developed stably, resulting in satisfactory seismic connection performance requirements specified in NEHRP. Meanwhile, the developed panel‐to‐base joint using the joint reinforcing bar and L‐shaped steel plates can be considered as a fixed end according to the analysis of the collapse mechanism, as with spliced sleeve or welding plate joints.
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