This study aimed to investigate the feasibility of using polypropylene fiber-cement-stabilized coastal clay as base-course material or foundation material for city sustainable development by assessing its mechanical performance. The influence of the number of freeze–thaw cycles and curing ages on the mechanical properties of ordinary cemented clay (OCC) and polypropylene fiber-cemented clay (PCC) was investigated by using unconfined compressive test. The experimental results show that the addition of fiber with 1% content can increase the strength as well as the ductility of cemented clay by 12.5% and 15.6%, respectively. The strength of PCC and OCC at 22d age was 1.5 times than at 7d age. Under differently timed freeze–thaw cycles, the mechanical performance of PCC is improved, and, better than that, OCC improves by 11.8% in strength, 16.5% in strain and by 5% in degree of damage, indicating that fiber can improve the freeze–thaw resistance of cemented clay. The frost resistance of PCC and OCC increases with the increase in curing age. Finally, the variation of strength of OCC was explained through the change of micro-structure while the strength enhancing mechanism of polypropylene fiber for cemented clay was also revealed.

Experimental instructional design is an important pedagogical component of university teaching and learning, an important means of cultivating students’ innovative spirit and practical skills, and has an important status and role that cannot be replaced by any other means of teaching and learning. Assessment for learning as learning, assessment for learning, and assessment as learning are three paradigms of educational assessment that complement each other in achieving curricular and pedagogical goals and together form learning-based assessment. As an important component of national science and technology development, measuring the effectiveness of laboratory instructional design in universities and research institutions is of special significance. This paper presents the authors’ research on the background, evaluation characteristics, evaluation content, and methods of experimental teaching evaluation in the information technology environment, with examples of their application.

In order to apply the fiber reinforced cement stabilized waste construction slurry more widely, it is crucial to evaluate its mechanical behavior under freeze and thaw (F–T) cycles. The mechanical properties of fiber cement-modified waste construction slurry (FCMS) under five different F–T cycles were studied using unconfined compressive strength tests. One fixed cement sample with five different types of polypropylene fiber was used in the FCMS. Using the meso random damage model, the two-stage damage stress–strain relationship of FCMS was established using particle swarm optimization (PSO). The results were as follows: (1) The mechanical properties of FCMS at room temperature and in an F–T environment were significantly improved by fiber; (2) The elastic modulus of FCMS decreased in the form of a power function with the increase of F–T cycles; (3) The fitting results of the two-stage F–T damage model based on meso random damage theory were in agreement with the measured data.

For the traditional laboratory manual management, there is a need for laboratory managers. The managers cannot keep abreast of the laboratory conditions, and forget to shut down and close the windows, causing waste of electricity and equipment damage. Based on NB-IoT and artificial intelligence, a laser intelligent management system is developed using technologies such as network communication, intranet, automatic control, artificial intelligence, and software. Through this system, administrators can grasp the situation of the laboratory in real time, realize the intelligent management of the laboratory, improve the scientific management of the laboratory, improve the work efficiency of laboratory technicians, and better play the laboratory as an important platform for scientific research training in colleges and universities. This paper aims to develop an intelligent laboratory management system based on NB-IoT and AI technology. Through this system, the manager can grasp the situation of the laboratory in real time, realize the intelligent management of the laboratory, and better play the role of the laboratory as an important platform for scientific research and talent training.