The utilizing of traditional chemical stabilizers could improve soil engineering properties but also results in brittle behavior and causes environmental problems. This study investigates the feasibility of the combined utilization of an ecofriendly biopolymer and fiber inclusions as an alternative to traditional cement for reinforcing soft soil. A series of unconfined compression tests were conducted to examine the combined effect of the biopolymer and fibers on the stress–strain characteristics, strength improvement, failure pattern, and reinforcement mechanism of soft soil. The results show that the biopolymer associated with fibers has an unconfined compressive strength similar to that of fiber-reinforced soil. However, it then increases with different curing times and conditions, which can be up to 1.5 MPa–2.5 MPa. The combined effect of fibers and the biopolymer is not simply equivalent to the sum of the effects of each individual material. The fiber shows its role instantly after being mixed into soil, whereas the effect of biopolymer gradually appears with sample curing time. The biopolymer plays a dominant role in increasing the peak unconfined compressive strength and brittleness of soil, while the amount of fiber is crucial for reducing soil brittleness and increasing ductility. It is shown that the biopolymer not only contributes to the particle bonding force but also facilitates the reinforcement efficiency of fibers in the soil. The fibers in return assist in reducing the soil brittleness arising from biopolymer cementation and provide residual resistance after post-peak failure.

Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have to be trained individually in each corresponding year. This study presented a novel strategy to map LULC classes without training samples or assigning parameters. First of all, several novel indices were carefully selected from the index pool, which were able to highlight certain LULC very well. Following this, a common unsupervised classifier was employed to extract the LULC from the associated index image without assigning thresholds. Finally, a supervised classification was implemented with samples automatically collected from the unsupervised classification outputs. Results illustrated that the proposed method could achieve satisfactory performance, reaching similar accuracies to traditional approaches. Findings of this study demonstrate that the proposed strategy is a simple and effective alternative to mapping urban LULC. With the proposed strategy, the budget and time required for remote-sensing data processing could be reduced dramatically.

The development of underground spaces generates large amounts of construction wastes, which are generally disposed for the landfill outside of the city in China. This study briefly reviewed a landslide tragedy of a CSW landfill in China; Lessons learned from the failure of risk assessment and preventions were discussed, which showed that two challenging strategies are significant for the risk identification and mitigation. One is the effective early warning system based on field large-deformation measurement; the other is the efficient risk assessment with internal deformation data. To this end, a new sensing method is developed for the large-deformation measurement inside CSW landfills. The proposed fibre-optic based transducer can measure large deformation of up to 1200 mm with a high accuracy. Finally, the risk assessment of CSW landfill was proposed with the combined use of field monitoring data and risk evaluation model. The theoretical equations for stability analysis and potential application framework for the application of this technique in the risk assessment of CSW landfill landslide are presented. This approach possesses the advantages of capturing excessive and irregular deformation of solid waste landfills and of efficient response for the risk mitigation with the help of early warning system.