American Psychological Association 6th edition

Gao, P., Song, Y., Wang, J., Yang, Z., Wang, K., & Yuan, Y. (2024). Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning. Buildings (2075-5309), 14(11), 3608-3626. https://doi.org/10.3390/buildings14113608

ISO-690 (author-date, English)

GAO, Pengfei, SONG, Yuanyuan, WANG, Jian, YANG, Zhiyong, WANG, Kai und YUAN, Yongyu, 2024. Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning. Buildings (2075-5309). 1 November 2024. Vol. 14, no. 11, p. 3608-3626. DOI 10.3390/buildings14113608.

Modern Language Association 9th edition

Gao, P., Y. Song, J. Wang, Z. Yang, K. Wang, und Y. Yuan. „Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning.“. Buildings (2075-5309), Bd. 14, Nr. 11, November 2024, S. 3608-26, https://doi.org/10.3390/buildings14113608.

Mohr Siebeck - Recht (Deutsch - Österreich)

Gao, Pengfei/Song, Yuanyuan/Wang, Jian/Yang, Zhiyong/Wang, Kai/Yuan, Yongyu: Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning., Buildings (2075-5309) 2024, 3608-3626.

Emerald - Harvard

Gao, P., Song, Y., Wang, J., Yang, Z., Wang, K. und Yuan, Y. (2024), „Prediction Model for the Chloride Ion Permeability Resistance of Recycled Aggregate Concrete Based on Machine Learning.“, Buildings (2075-5309), Vol. 14 No. 11, S. 3608-3626.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.