In order to achieve a sustainable mix design, this paper evaluates self-consolidating green concrete (SCGC) properties by experimental tests and then examines the design parameters with an artificial intelligence technique. In this regard, cement was partially replaced in different contents with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Moreover, fresh and hardened properties tests were performed on the specimens. Finally, an adaptive neuro-fuzzy inference system (ANFIS) was developed to identify the influencing parameters on the compressive strength of the specimens. For this purpose, seven ANFIS models evaluated the input parameters separately, and in terms of optimization, twenty-one models were assigned to different combinations of inputs. Experimental results were reported and discussed completely, where furnace slag represented the most effect on the hardened properties in binary mixes, and volcanic powder played an effective role in slump retention among other cement replacements. However, the combination of micro-silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs. Furthermore, ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC. Finally, when compared with other additive powders, the combination of micro-silica with volcanic powder provided the most strength, which has also been verified and reported by the test results.

The development of deep learning technology has promoted the wide application of face recognition in many scenarios such as mobile payment and social media, but the security of user data is facing great challenges. To protect the privacy of users, face authentication cannot be operated in plaintext. To solve this problem, a face feature ciphertext authentication scheme based on homomorphic encryption is proposed. First, the face image feature extraction is completed based on a deep learning model. Second, the face features are packaged into ciphertext by using homomorphic encryption and batch processing technology, and the face feature ciphertext is saved in the database of the cloud server. Third, combined with automorphism mapping and Hamming distance, a face feature ciphertext recognition method is designed, which can complete face recognition in the case of ciphertext. Finally, the integrity and consistency of face feature ciphertext recognition results before and after decryption are guaranteed by the one-time MAC authentication method. The whole framework can finish identity recognition without decrypting face feature coding, and the homomorphic ciphertext of face feature coding is saved in the database, so there is no risk of face feature coding leakage. Experiments show that the system has met the requirements of real application scenarios.