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In this paper, a combination of artificial neural network and ant colony optimization (ANN-ACO) was used for dynamic conditions of retaining wall structures. The retaining walls produce different responses to dynamic loads. The applied data of this study comprising of wall height and thickness, soil density, internal friction angle, and stone density. The walls were designed in a variety of dynamic conditions. Various conditions were considered for the design of the retaining wall structures. Then, an extended data set was created for the next step. After that, the new systems were implemented using optimized artificial intelligence techniques. The neural network provided strong relationships between various wall parameters. The design of various networks in the present research led to the best evaluation of the dynamic conditions of the retaining walls. Under these conditions, an ACO was used for optimal design. Effects of parameters varied due to different wall conditions when dynamic loads were considered. Therefore, the impact of parameters was evaluated using hybrid ANN-ACO to increase the efficiency. These designs provided more control over dangers by dynamic loads of a retaining wall structure.

With the development of globalization, the contacts and exchanges between countries in the world are getting closer and more frequent. As the most widely used language in the world, English is favored and valued by many non-English speaking countries. In order to facilitate communication, people use computers technology has developed a computer translation tool. The purpose of this article is to explore the difference between machine translation and computer-assisted translation in computer translation, compare the advantages and characteristics of the two in English translation, and point out the direction for future English translation trends. This article uses Google Translator, Youdao Translator and Baidu Translator in machine translation software and Wordfast, WordFisher and iCAT Huoyun Translator in computer-aided translation software as the experimental research objects to compare and analyze the different features and advantages between machine translation and computer-aided translation, and compare the error rate and match rate of the two in English translation. Finally, the experimental results show that the total number of errors in computer-assisted translation software translation is less than that of machine translation software. Among them, WordFisher has the least number of errors, 31 errors in vocabulary, 25 errors in sentences, and 3 errors in punctuation, a total of 59 errors; with the improvement of translation matching rate, the number of translation errors of English professional terminology of machine translation software is increasing, and the error rate is getting higher and higher, when the matching rate is 90-100, Google Translate becomes the software with the most translation errors, and the number of errors reached 78. Studies have shown that computer-assisted English is better and more accurate than machine translation.

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