Yang Yu

Laterally loaded piles, which are commonly used in sandy stratum foundations, are particularly susceptible to necking defects during cast-in-place installa

Highway landslides in hilly regions pose significant risks to transportation infrastructure. Current monitoring systems, confined to jurisdictional boundaries, neglect cross-administrative slopes, creating systemic gaps in hazard management. This oversight is exacerbated in densely vegetated areas lacking standardized detection protocols, where external landslides may trigger destructive debris flows impacting highways. A two-stage framework is proposed in this work. Failure mechanism of failure landslides, located beyond the jurisdictional redline of a highway, are first analyzed based on small baseline subset synthetic aperture radar interferometry (SBAS-InSAR). The obtained deformation thresholds together with SBAS-InSAR are then used to quantify pre-failure deformation and identify landslide risk along the same highway. Results show that vegetation destruction and rainfall are primary triggers for the studied landslide and subsequent debris flows. SBAS-InSAR revealed an annual deformation threshold of 21.4–32.8 mm/year. High-risk slopes within 500 m of the highway with sparse vegetation require prioritized monitoring during rainy seasons. This approach offering actionable criteria for authorities to monitor extra-jurisdictional slopes. By integrating failure mechanism analysis with deformation thresholds, the study bridges jurisdictional gaps in infrastructure risk management, enhancing resilience against previously overlooked cross-boundary hazards.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful and cost-effective technology to monitor ground deformation. Its accuracy is highly influenced by the atmospheric characteristic of the monitoring area. Separating the true ground deformation from atmospheric signals remains one of the major challenges in the application of InSAR. In this paper, the phase-based linear model, high-spatial resolution weather model (MERRA-2 and GACOS) and combination of the MERRA-2 and phase-based linear model are selected, and their performances in reducing the tropospheric delay are assessed based on the detrending standard deviation (DStd) of all Persistent Scattered (PS) points. A framework for the assessment is proposed and applied to a selected region of Shaoxing, Zhejiang Province, China. A total of 26 Sentinel-1A images are used and processed by the method of PS-InSAR. It is found that the phase-based linear model outperforms the other models by at least 6.6% if the whole monitoring time span of the SAR images in the study area is considered. The proper tropospheric correction model in different seasons is not the same. The phase-based linear model is robust against the variations in the atmospheric characteristics of the four seasons.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful and cost-effective technology to monitor ground deformation. Its accuracy is highly influenced by the atmospheric characteristic of the monitoring area. Separating the true ground deformation from atmospheric signals remains one of the major challenges in the application of InSAR. In this paper, the phase-based linear model, high spatial-resolution weather model (MERRA-2 and GACOS) and combination of MERRA-2 and phase-based linear model selected, and their performances in reducing the Tropospheric delay are assessed based on detrending standard deviation (DStd) of all Persistent Scattered (PS) points. A framework for the assessment is proposed and applied to a selected region of Shaoxing, Zhejiang Province, China. 26 Sentinel-1A images are used and processed by the method of PS-InSAR. It is found that the phase-based linear model outperforms the other models by at least 6.6% if the whole monitoring time span of the SAR images in the study area are considered. The proper tropospheric correction model in different seasons is not the same. The phase-based linear model is robust against the variations of atmospheric characteristics of the four seasons.

Safety status of artificial slopes is significant for the operation and maintenance of highway to mitigate the risk; thus, slope hazard identification is necessary. In order to realize large-area and low-cost application for regional highway, taking the Longqing Highway (length of 55 km) as a case study, the SBAS-InSAR (Small Baseline Subset-Interferometric Synthetic Aperture Radar) technique is adopted to detect the ground deformation and conduct hazard identification based on slope dip, aspect, geological data and historical hazard record. Field survey is carried out to verify the identified potential hazards. Results show that the detected potential hazards are distributed mainly in the areas consisting of granite residual and the Quaternary soil. Six potential hazards identified by the SBAS-InSAR-based method are roughly in accordance with the on-site verification. It is suggested that the SBAS-InSAR technique has the ability to obtain the slope deformation accurately and reveal the safe condition of the slopes. The SBAS-InSAR technique can be suitable for assistance in regional highway slope inspection.