This paper studies the excavation collapse at the Xianghu subway station on Hangzhou metro line 1. The objective is to present an overview of this case study and discuss the cause of the failure. Through field investigation and preliminary analysis, the reasons for the excavation collapse were the misuse of the soil parameters, over excavation, incorrect installation of steel struts, invalid monitoring data, and inadequate ground improvement. Finally, a small strain constitutive model was used for further analysis. In order to estimate damage efficiently, the orthogonal array (OA) was introduced for screening the key factor in the numerical experiments. Six estimated indexes including deformations and internal forces of the excavation were taken, and the effectiveness of four factors which may cause the collapse was evaluated. Through numerical experiments and interaction analysis, it is found that the deformation and internal force can be well controlled by jet grouting of the subsoil under the final cutting surface, but increasing the improvement ratio of the jet grouting cannot help optimize the excavation behavior efficiently, and without jet grouting and the fourth level struts, the deformation and internal force of the excavation in this case will far surpass the allowable value.

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

This paper describes recent advances in the effect of soil creep on the time-dependent deformation of deep braced excavation. The effect of soil creep is generally investigated using the observational method and the plain-strain numerical simulation method. The observational method is more applicable for deep braced excavations in soft clays constructed using the top-down method. The plain-strain numerical simulation method can be conveniently used for parametric analysis, but it is unable to capture the spatial characteristics of soil creep effect on lateral wall deflections and ground movements. The additional lateral wall deflections and ground movements that are generated due to the soil creep effect can account for as large as 30% of the total displacements, which highlights the importance of considering the effect of soil creep in deep braced excavations through soft clays. The magnitude of the displacements due to soil creep depends on various factors, such as excavation depth, elapsed period, unsupported length, and strut stiffness. Parametric analyses have indicated several effective measures that can be taken in practice to mitigate the detrimental effect of soil creep on the deformation of deep braced excavation. Based on the literature review, potential directions of the related future research work are discussed. This paper should be beneficial for both researchers and engineers focusing on mitigating the adverse effect of soil creep on the stability of deep braced excavations.

The static drill rooted nodular (SDRN) pile is a new type of composite pile that consists of a precast pile and surrounding cemented soil. Its cost advanta

静钻根植竹节桩是一种由预制桩和水泥土组成的组合桩基, 其桩身具有预制桩-水泥土和水泥土-桩周土体两个接触面。本文以桩土接触面剪切试验、模型试验和现场试验数据为基础, 提出一种静钻根植竹节桩简化沉降计算方法, 为静钻根植竹节桩的沉降计算提供依据; 并通过所提出的简化计算方法对静钻根植竹节桩桩端水泥土扩大头以及桩周水泥土直径对静钻根植竹节桩承载性能的影响进行研究。 1. 通过剪切试验对预制桩-水泥土接