The Tehran basin has been increasingly affected by subsidence during the last few decades due to groundwater withdrawal. Hence, the study of the strength of the power towers (PTs) of transmission lines, as vital structures, is an important subject. In this paper, the persistent scatterer interferometry (PSI) method was applied on data stacks from two satellites (i.e., X-band COSMO-SkyMed (CSK) and C-band Sentinel-1A (S-1A)) obtained between 2014 and 2016 to investigate the deformation and the exact amount of displacement in each PT of the area of interest. Based on the results, during the same time interval (between October 2014 and February 2016), the vertical velocities calculated using CSK and S-1A were about −86 and −79 mm/y, respectively. Although the CSK data analysis resulted in a better displacement interpretation of PTs, due to its high resolution and shorter wavelength, the S-1 data analysis also demonstrated sufficient persistent scatterer (PS) points. The research proves that most of the PTs along a transmission line are affected by high land subsidence, which puts them in a serious jeopardy. They must be constantly monitored to ensure their safety and accurate operation. The results are in complete agreement with information of the existing global positioning system (GPS) station in our study area and also the observations of two piezometric wells with declining trends in the groundwater reservoir, which has the greatest effect on the subsidence rate in this area. The analysis revealed that the strength of PTs is at a high risk.

In this paper, land subsidence susceptibility was assessed for Shahryar County in Iran using the adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. Another aim of the present paper was to assess if ensembles of ANFIS with two meta-heuristic algorithms (imperialist competitive algorithm (ICA) and gray wolf optimization (GWO)) would yield a better prediction performance. A remote sensing synthetic aperture radar (SAR) dataset from 2019 to 2020 and the persistent-scatterer SAR interferometry (PS-InSAR) technique were used to obtain a land subsidence inventory of the study area and use it for training and testing models. Resulting PS points were divided into two parts of 70% and 30% for training and testing the models, respectively. For susceptibility analysis, eleven conditioning factors were taken into account: the altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance to stream, distance to road, stream density, groundwater drawdown, and land use/land cover (LULC). A frequency ratio (FR) was applied to assess the correlation of factors to subsidence occurrence. The prediction power of the models and their generated land subsidence susceptibility maps (LSSMs) were validated using the root mean square error (RMSE) value and area under curve of receiver operating characteristic (AUC-ROC) analysis. The ROC results showed that ANFIS-ICA had the best accuracy (0.932) among the models (ANFIS-GWO (0.926), ANFIS (0.908)). The results of this work showed that optimizing ANFIS with meta-heuristics considerably improves LSSM accuracy although ANFIS alone had an acceptable result.

We would like to thank our colleagues for their comment, as we believe that this discussion further highlights the importance of innovative research in the emerging field of InSAR applications to civil engineering structures. We discuss the statement from Lanari et al. (2020): “Our analysis shows that, although both the SBAS and the TomoSAR analyses allow achieving denser coherent pixel maps relevant to the Morandi bridge, nothing of the pre-collapse large displacements reported in Milillo et al. (2019) appears in our results”. In this reply we argue that (1) they cannot detect the pre-collapse movements because they use standard approaches and (2) the signals of interest become observable by changing the point of view.

We present a methodology for the assessment of possible pre-failure bridge deformations, based on SyntheticAperture Radar (SAR) observations. We apply this methodology to obtain a detailed 15-year survey of the Morandi bridge (Polcevera Viaduct) in the form of relative displacements across the structure prior to its collapse on August 14th 2018. We generated a displacement map for the structure from space-based SAR measurements acquired by the Italian constellation COSMO-SkyMed and the European constellation Sentinel-1A/B over the period 2009-2018. Historical satellite datasets include Envisat data spanning 2003-2011. The map reveals that the bridge was undergoing an increased magnitude of deformations over time prior to its collapse. This technique shows that the deck next to the collapsed pier was characterized since 2015 by increasing relative displacements. The COSMO-SkyMed dataset reveals the increased deformation magnitude over time of several points located near the strands of this deck between 12th March 2017 and August 2018.

Structural monitoring of surface building displacements is a significant component of the total financial investment for underground construction projects in urban areas. While traditional monitoring requires in-situ (terrestrial) measurements and trigger levels based on preliminary evaluation of vulnerable structures, very recent advances in Interferometric Synthetic Aperture Radar (InSAR) techniques enable remote monitoring over extensive areas, providing rapid, semi-automatic, and dense measurements with millimetre accuracy. Despite the well-established use of InSAR in geophysical applications, only a few studies are currently available on the use of satellite-based monitoring for the assessment of building deformations and structural damage. The aim of this project is to investigate the potential of InSAR monitoring data as an input to post-tunnelling damage assessment procedures. First, InSAR-based measurements of building displacements, induced by the excavation of Crossrail tunnels in London, were acquired and processed. Then, following the definition of a step-by-step procedure, the satellite-based building displacements were used to evaluate structural deformation parameters typically used in extensive damage assessment procedures. Results show that the number of available measures per single building can enable the estimation of deformation parameters, a capability that is not economically feasible for large scale projects using traditional monitoring systems. The comparison with greenfield predictions offers new insight into the effect of soil-structure interaction and demonstrates the suitability of InSAR monitoring for post-tunnelling damage assessment of structures. The outcome of this work can have a significant economic impact on the construction industry and can advance the knowledge of building and infrastructure response to ground subsidence.

The increasing demand for underground infrastructure should be supported by innovation in monitoring and damage assessment solutions to minimise damage to surface structures caused by ground settlements. This paper evaluates the use of multitemporal synthetic aperture radar interferometry (MT-InSAR) to calculate tunnelling-induced deformations of buildings. The paper introduces a step-by-step procedure to use InSAR displacements as an input to the structural damage assessment. After a comparison between traditional and InSAR monitoring data for the London area during the Crossrail excavation, the high resolution, high density InSAR-based displacements were used to evaluate the building deformations for a number of case studies. Results demonstrate the quality of information provided by InSAR data on soil-structure interaction mechanisms. Such information, essential to evaluate current damage assessment procedures, is typically only collected for relatively few buildings due to the cost of traditional monitoring. A comparison between damage indicators derived from greenfield assumptions and building displacements quantifies the practical benefit of the proposed step-by-step procedure. This work aims at filling the gap between the most recent advances in remote sensing and the civil engineering practice, defining the first step of an automated damage assessment procedure which can impact large scale underground projects in urban areas.

Spaceborne multi-temporal interferometric synthetic aperture radar (MT-InSAR) is a monitoring technique capable of extracting line of sight (LOS) cumulative surface displacement measurements with millimeter accuracy. Several improvements in the techniques and datasets quality led to more effective, near real time assessment and response, and a greater ability of constraining dynamically changing physical processes. Using examples of the COSMO-SkyMed (CSK) system, we present a methodology that bridges the gaps between MT-InSAR and the relative stiffness method for tunnel-induced subsidence damage assessment. The results allow quantification of the effect of the building on the settlement profile. As expected the greenfield deformation assessment tends to provide a conservative estimate in the majority of cases (~71% of the analyzed buildings), overestimating tensile strains up to 50%. With this work we show how these two techniques in the field of remote sensing and structural engineering can be synergistically used to complement and replace the traditional ground based analysis by providing an extended coverage and a temporally dense set of data.