This study aimed to research the whole stress–strain curves of coral Fly Ash-Slag Alkali-Activated Concrete (CAAC) in different strength grades. Fly ash-slag alkali-activated concrete was used as cementing materials to produce coral aggregate concrete. A monotonic loading test was conducted on the prismatic specimens to obtain elastic modulus (E_c), Poisson’s ratio (μ) and the constitutive relationship of CAAC under uniaxial compression. When the strain of CAAC reached the maximum value, the specimen was split and damaged rapidly. As the strength grade increased, the ratio of residual stress (σ_ri) to peak stress (σ_0i) decreased in the range of 0.17–0.28. The E_c of CAAC increased gradually, and μ increased to the peak value and then decreased. According to the test results, the constitutive equation of CAAC can be expressed by piecewise expression, which can better reflect all the experimental characteristics. It was also found that CAAC has many similar characteristics with coral concrete and lightweight aggregate concrete. To improve the strength and toughness of CAAC, some fibers, such as organic fiber, can be added to expand the application of CAAC in engineering projects.

Machine Learning (ML) has developed rapidly in recent years, achieving exciting advancements in applications such as data mining, computer vision, natural language processing, data feature extraction, and prediction. ML methods are increasingly being utilized in various aspects of seismic engineering, such as predicting the performance of various construction materials, monitoring the health of building structures or components, forecasting their seismic resistance, predicting potential earthquakes or aftershocks, and evaluating the residual performance of post-earthquake damaged buildings. This study conducts a scientometric-based review on the application of machine learning in seismic engineering. The Scopus database was selected for the data search and retrieval. During the data analysis, the sources of publications relevant to machine learning applications in seismic engineering, relevant keywords, influential authors based on publication count, and significant articles based on citation count were identified. The sources, keywords, and publications in the literature were analyzed and scientifically visualized using the VOSviewer software tool. The analysis results will help researchers understand the trending and latest research topics in the related field, facilitate collaboration among researchers, and promote the exchange of innovative ideas and methods.