The volume of shield tunnel spoil (STS) is very large, its effective management is difficult, and it even causes environmental pollution. In this study, to achieve its recycling, a novel controlled low strength material (CLSM) was prepared by utilizing high fine-grained STS as partial aggregates instead of sand, and its engineering performance was thoroughly evaluated. In the process of mix proportion design, key parameters such as the STS-tototal aggregate ratio (TS/TA), foam agent content (F), water-to-binder ratio (W/B), binder-to-total aggregate ratio (B/TA), and fly ash-to-cement ratio (FA/C) were employed. Workability aspects (i.e., flowability, bleeding rate, and setting time) and physical and mechanical properties (i.e., unconfined compressive strength and density) were evaluated. Additionally, the pH of bleeding and leachate, as well as the impact of foam agent content on CLSM properties, were examined. The findings revealed that an increase in the TS/TA ratio was associated with a decrease in flowability, density, and compressive strength, as well as an extension in setting time. The CLSM, with a flowability range of 150-300 mm, exhibited a bleeding rate below 2%, setting times between 3.6 and 6.1 hours, 28-day strength ranging from 1.06 to 3.24 MPa, and fresh density ranging from 1810 to 2060 kg/m3. Generally, these results met the required specifications, although the fresh density was slightly lower. The pH results indicated that the CLSM is non-corrosive. Furthermore, our investigation highlighted the substantial influence of foam agent content on flowability and setting time. An increase of 0.1 parts per thousand in the proportion of foam agent within the total aggregates resulted in a flowability increase of 2.1-2.6 mm and a setting time increase of 4.25-4.99 minutes. Therefore, it is feasible to utilize high fine-grained STS in the production of CLSM.

Driven by external compressions or shears, the granular material composed of non-convex shapes often undergoes self-assembly and becomes entangled, resulting in denser packings. In this work, the biaxial compression of monophasic and binary mixture composed of 2D intersecting crosses have been simulated via the discrete element method. As the aspect ratio increases, both the yield strength and elastic modulus initially increase before reaching a peak at w = 0.5. In binary mixtures, an antagonistic effect has been observed in mechanical properties of granular material. The decrease in strength and stiffness in binary mixtures can be attributed to the constraints imposed by multi-point contacts, along with the local order degree, which is particularly unique for non-convex particle packings. Building upon this understanding, we have extended the empirical formula into predicting the mechanical behavior of non-convex binary mixtures. This extension incorporates an additional non-linear term determined by both the shape factor and component fraction.

Landslides are downward movements of soil, rock, and debris along slopes, and pose significant risks to communities, especially in inhabited areas as they can cause severe damage, including the destruction of infrastructure and loss of life. Solutions for prediction and mitigation strategies are crucial, which often relying on rainfall forecasting and monitoring through sensor and IoT technologies. However, such solutions can be costly and challenging to implement, particularly in developing countries. By taking advantage of 5G networks, this paper proposes an innovative Received Signal Strength Indication (RSSI)-based solution to estimate the landslide risk and assist in mitigation actions in advance. Our solution also incorporates Software-Defined Networks (SDN) to manage the collected real-time data, compute the landslide risk, and notify users in the affected area. Results show that adopting 5G RSSI can significantly improve the accuracy in detecting rains and landslides, as shown by a high Pearson correlation coefficient (0.984), a Mean Squared Error (MSE) of 0.0087, and a coefficient of determination (R-2) of 0.6185 for the RSSI-bases solution.