The central concern is an “underground monitoring gap” created by three linked failures: cities often cannot directly observe subsurface change, relevant information is scattered across incompatible agencies and technical systems, and ownership, regulatory authority, and responsibility for resulting effects can remain unclear. Tunnels, basements, utility corridors, foundations, geothermal installations, and other underground development can add occupied volume or intensify urban functions without altering a city’s visible footprint. This creates an incomplete picture of urban growth, including cumulative effects on groundwater, subsurface temperature, ground stability, essential services, and communities that may experience disruption or unequal exposure to risk.

The proposed monitoring system treats the city as a three-dimensional system rather than a surface plan with a hidden layer. Its first analytical dimension, spatial occupation, asks what exists below ground, where it is located, and how its volume, depth, function, and use change over time. The second examines environmental interaction, including underground development’s relationships with groundwater, heat, and ground conditions. The third addresses infrastructure interdependence by tracing shared corridors, service dependencies, maintenance access, emergency access, and the possibility that a change in one buried system could create cascading risks elsewhere. The fourth asks whether responsibility can be traced to identifiable owners, operators, regulators, and other institutions, including for maintenance, emergency action, remediation, and eventual decommissioning.

Social consequences cut across all four dimensions, the paper says. A volumetric inventory may reveal that underground construction affects particular neighborhoods more severely, interrupts essential services, or limits public access to information about works and risks. Reliable monitoring therefore requires more than a spatial database: records from different sources need shared identifiers and spatial references, clear provenance, explicit uncertainty, and designated custodians who can keep them current. Under the proposed standards, an institutional indicator should not treat the mere assignment of a duty as proof that it has been fulfilled. Measurements, modeled effects, and inferred causes must also be labeled separately, particularly where several processes could produce the same surface signal.

Earth observation has a focused but limited role in the system. Interferometric synthetic aperture radar, or InSAR, and related techniques can detect ground deformation and other surface responses associated with underground activity. These signals can support initial screening, identify areas requiring investigation, and monitor possible surface impacts. They cannot, by themselves, reveal the geometry, function, or condition of most underground assets or establish what caused an observed deformation. The framework therefore combines satellite observations with boreholes, sensors, field measurements, engineering records, environmental models, asset inventories, and institutional documents. Attribution still requires technical and environmental evidence, while decisions about legal status and responsibility depend on complementary land and administrative records.

The framework also proposes “responsible visibility,” a governance mechanism for determining who should receive underground information and at what level of detail. Operators and emergency responders may need detailed operational data, while public authorities may require asset-level information for permitting and coordination. Researchers and affected communities may need enough access to conduct analysis, oversight, and response without exposing sensitive infrastructure. Public reporting can focus on broader indicators such as development, risk, compliance, and assigned responsibility. Any restriction should have a narrow justification, a defined group of authorized users, and a scheduled review, with redaction, aggregation, or controlled access used where appropriate.

“Volumetric accountability” supplies the second governance mechanism by connecting an underground asset and its effects to identifiable actors and enforceable obligations. Approval and operating records should specify duties for monitoring, maintenance, remediation, and closure, including procedures for transferring those duties when an operator or institution changes. Where obligations may persist for many years, the paper argues, financial assurance or cost-recovery arrangements can help preserve the resources needed for monitoring, remediation, or closure. Communities also need procedures through which they can obtain information, raise concerns, and seek corrective action. The framework identifies responsible actors without assuming that formal assignments are adequately funded, enforced, or completed in practice.

Operationally, the Perspective arranges these elements into a five-stage cycle. The first establishes a baseline inventory of assets, institutional records, uncertainties, and missing information, distinguishing verified facts from outdated, estimated, or incomplete records. The second maps priorities and risks by identifying intersections among underground occupation, hazards, infrastructure dependencies, and exposed populations. The third integrates evidence and assigns responsibility, linking assets to ownership, regulatory authority, environmental information, and data stewards while preserving provenance and uncertainty. The fourth establishes access classifications, disclosure practices, and safeguards. The fifth applies the evidence to decisions and periodic review, checking whether lifecycle duties, inspections, maintenance, financial assurance, decommissioning plans, and remediation obligations have been fulfilled.

The paper presents three cities as illustrative starting points rather than completed demonstrations of the system. New York City’s recent work on subway flooding, service disruption, and coastal-flood resilience could be connected to records of subway assets, drainage conditions, and institutional duties. In Lyon, records of basements and other underground structures offer an entry point for examining cumulative effects on groundwater flow, model uncertainty, and responsibility for responses. Singapore’s detailed records of underground utilities, already linked to legal rights, responsibilities, and organizational roles, could be extended with environmental conditions, infrastructure dependencies, information-access rules, and long-term accountability. These cases show how cities might begin with capabilities they already possess, although the article does not report a completed implementation or measured governance outcome in any of them.

The proposal is significant because it changes what counts as urban expansion and what cities must measure to govern it. Conventional indicators can register little change while underground occupation, environmental effects, infrastructure pressures, and public obligations continue to grow. Implementation will nevertheless vary with the quality of local records and the ability of institutions to coordinate information, and the framework has not yet been validated as a universal system. The paper argues that cities could begin with high-risk assets in transit corridors, flood-prone districts, or places experiencing subsidence or groundwater stress, then expand coverage as records, responsibilities, and update procedures improve. Comparative research would still need to test which indicators work consistently across cities and whether the approach improves the continuity and traceability of evidence and responsibility. The immediate advance is therefore conceptual and operational: it supplies a disciplined way to detect hidden urban change, combine evidence, protect sensitive information, and reduce the chance that long-term underground costs and obligations remain unresolved.

Subject of Research: Proposed framework for monitoring and governing durable growth in urban underground space.

Article Title: Seeing cities in depth: subsurface urban expansion and the case for volumetric monitoring and accountability

Article References: Gil, A. G. C. (2026). Seeing cities in depth: subsurface urban expansion and the case for volumetric monitoring and accountability. Frontiers in Sustainable Cities, 8, Article 1878583. https://doi.org/10.3389/frsc.2026.1878583

Image Credits: AI Generated

DOI: 10.3389/frsc.2026.1878583

Keywords: subsurface urban expansion, volumetric monitoring, underground space, urban governance, remote sensing, InSAR, ground deformation, groundwater, infrastructure accountability, responsible visibility, Seeing, cities

Developed by Qingsong Du and released under the GNU General Public License, LiCSAR-WinFetch version 1.3.0 is a Python workflow that handles the unglamorous but critical stage between the public archive and the time-series analysis packages researchers actually use. It discovers and resolves remote products, downloads a selected single-frame dataset, organises the results into the exact directory hierarchy expected by LiCSBAS — the widely adopted open-source package for quality control, displacement time-series estimation and velocity mapping — and validates the structure of what has arrived on disk. Crucially, it also does something most downloaders do not: it can plan a wide-area study entirely from inventory metadata before a single large raster file is transferred.

The motivation is practical. A single LiCSAR frame can contain hundreds of acquisition dates and thousands of interferogram pairs, and a browser-based retrieval session or a minimal script offers little protection against interrupted connections, partially written files, repeated transfers and uncertainty about which missing objects are actually required. For a time-series analysis to work, every interferogram pair needs both its unwrapped-phase raster and its coherence raster; if either is absent, the pair is structurally incomplete. An optional atmospheric correction product, by contrast, should be reported as unavailable without being misclassified as a failed required pair. LiCSAR-WinFetch keeps these product roles strictly separate, ending each file in an explicit state: downloaded, already complete, planned, unavailable or failed.

The tool also confronts a messier reality of public archives: they change. The COMET-LiCS portal reported a storage-system migration in October 2025 and advised users to update their download code. During such transitions, an expected file may appear as a direct object, a redirected target, or a small HTML or plain-text manifest containing the real object URL. A naive client that treats every entry as an ordinary directory can falsely classify available data as missing. LiCSAR-WinFetch’s resolver tests the expected URL and supported storage variants, resolves HTML anchors relative to the final response location, searches plain-text manifests for absolute targets, and de-duplicates candidates before any transfer begins. Its behaviour under these cases is pinned down by a fifteen-test deterministic suite, all of which passed for version 1.3.0.

Download integrity receives equally careful treatment. Transfers run through a configurable thread pool, with bytes first written to a temporary .part file; the final filename appears only after a size check against the remote object succeeds. If a partial file exists, the client sends an HTTP Range request to resume from the local byte count, and if a server ignores that request and returns a complete object, the temporary file is reopened cleanly rather than corrupted by incompatible appending. Every run produces a machine-readable CSV report of file-level outcomes, a text summary and a log — records that can be archived alongside a study so that a later investigator can distinguish a changed remote object from an interrupted local transfer or a deliberately excluded optional product.

The most scientifically interesting component, however, is the multi-frame temporal planner. Wide-area studies often span many adjacent radar frames, and those frames may cover different date ranges. If neighbouring frames sample different seasonal windows or event intervals, a nominally common analysis can silently compare incomparable things. The planner scans the remote inventories of an entire frame group and computes the maximum common calendar interval shared by all frames, the exact intersection of acquisition dates and interferogram-pair networks within a same-orbit group, and a retention statistic describing how much of the smaller pair network survives strict common-pair selection. It then evaluates four temporal-selection modes of increasing strictness — independent, common-period, common-epochs and exact common-pairs — and reports the graph-theoretic consequences of each.

A demonstration on 22 LiCSAR frames covering the Qilian Mountains — eleven ascending and eleven descending frames in twelve relative-orbit groups — shows why this planning layer matters. All 22 inventory scans completed without downloading any imagery, revealing a 22-frame maximum common calendar interval from November 2014 to September 2023. Across the ten two-frame groups, exact common-pair retention ranged from 71.4 percent to 97.1 percent, and nine of the resulting networks stayed connected. But the 106D group delivered a cautionary result: it retained 94.0 percent of the smaller within-period pair network — 1,970 of 2,096 pairs — yet the strict filtering fragmented the graph into four connected components and left three common acquisition dates isolated. Two of those dates were already isolated in the shorter frame’s independent network; the third, August 2023, became isolated only after pair intersection. A high retention percentage, in other words, can coexist with hidden fragmentation that would only surface after terabytes of data had been downloaded.

The package’s interoperability claims were tested against the maintained LiCSBAS step 01 downloader on a bounded benchmark: frame 124D_04854_171313 over four acquisition epochs in September and October 2016, yielding six interferogram pairs. Both tools produced all 20 expected output paths, and 19 of the 20 files were byte-identical by SHA-256 hashing, including all 12 interferogram products. The single difference was the representative multilook-intensity raster, traced to a differing source-epoch selection rule rather than an incomplete transfer. The LiCSAR-WinFetch output then passed through LiCSBAS’s step 02 preprocessing script under Ubuntu 22.04 on the Windows Subsystem for Linux without any manual renaming, processing all six interferograms and returning exit code zero. Cross-platform continuous integration on Windows, Ubuntu and macOS under Python 3.10 and 3.12 passed all six jobs, confirming that the tool, though born on Windows desktops, is genuinely platform-neutral.

The authors are careful about scope. LiCSAR-WinFetch does not generate interferograms, assess phase-unwrapping quality, estimate deformation time series, harmonise cross-frame references, mosaic results, model the atmosphere or perform line-of-sight decomposition. A structurally complete directory certifies only that the requested products are present and organised, not that the underlying geophysical measurements are sound. The validation is bounded to the tested archive representations, frame selections and LiCSBAS step 02, and says nothing about transfer speed or future archive compatibility. Yet within those limits, the tool changes the order of operations for wide-area Sentinel-1 studies: researchers can now check calendar overlap, network connectivity and pair retention before committing storage and processing resources, and archive the machine-readable evidence of every decision. For studies of tectonic deformation, volcanic unrest, subsidence and infrastructure monitoring, that makes the path from remote inventory to analysis-ready input explicit, inspectable and reproducible — a quiet but meaningful advance in how satellite radar science gets done.

Subject of Research: Open-source software for reproducible acquisition, temporal planning and LiCSBAS-ready organisation of Sentinel-1 InSAR products from the COMET-LiCS portal

Article Title: LiCSAR-WinFetch: Reproducible LiCSAR acquisition, multi-frame temporal planning, and LiCSBAS-ready organisation of LiCSAR products

Article References: Du, Q. (2026). LiCSAR-WinFetch: Reproducible LiCSAR acquisition, multi-frame temporal planning, and LiCSBAS-ready organisation of LiCSAR products. SoftwareX, 36, Article 103119. https://doi.org/10.1016/j.softx.2026.103119

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103119

Keywords: InSAR, LiCSAR, LiCSBAS, Sentinel-1, LiCSAR-WinFetch, open-source software, radar interferometry, ground deformation, reproducibility, temporal planning, COMET-LiCS portal, FAIR data

The study, published in the journal Natural Hazards, was led by Ademola Ibironke and Xing Wu of the Department of Civil and Environmental Engineering, together with Fayez Albalawi, Aawaz Gautam, and earth scientist Joseph Kruger. Their focus was Jefferson County, a low-lying expanse of Gulf Coast wetlands, refineries, and pipeline rights-of-way where even modest vertical ground motion can threaten buried infrastructure, alter drainage patterns, and amplify flood risk. Rather than relying on a single snapshot of the terrain, the team exploited a powerful but underused resource: two independent airborne LiDAR surveys of the county, one acquired in 2006 by the Texas Water Development Board and another in 2017, eleven years apart.

Airborne LiDAR works by firing hundreds of thousands of laser pulses per second from an aircraft toward the ground and timing the returning echoes to reconstruct the three-dimensional shape of the landscape as a dense point cloud. When two such surveys are captured years apart, subtracting one from the other yields a map of surface-elevation change at decimeter-scale vertical resolution, far finer than most satellite or surveying alternatives. The catch is the data volume. A single LiDAR tile covering a fraction of a county can contain tens of millions of points, each tagged with coordinates, classification codes, and metadata. Aligning two point clouds acquired with different sensors, flight paths, and classification conventions, then computing reliable elevation differences, is exactly the kind of repetitive computational choreography that humans perform slowly and inconsistently.

The researchers set up a head-to-head comparison between two workflows. The first was a conventional approach built in ArcGIS Pro, the industry-standard geographic information system, in which an analyst manually loads the LAS-format point clouds, filters them, interpolates them into digital elevation models, and runs the differencing tools step by step. The second was a Python-enhanced workflow that wrapped the same core geoprocessing operations into an automated script, chaining together point-cloud processing, spatial matching between the two survey epochs, and the final elevation-change calculations without continuous human supervision. The goal was not to invent new science so much as to industrialize an existing one, turning a bespoke analysis into something a monitoring agency could run repeatedly across hundreds of tiles.

The headline result is strikingly simple: for a representative single LiDAR tile processed through both pipelines, the Python-enhanced workflow cut processing time by approximately 77 percent. That figure matters because the bottleneck in LiDAR-based change detection has never been the physics of the laser; it has been the hours of analyst time consumed by clicking through menus, waiting for tools to finish, and re-running steps when parameters change. By automating the chain, the team effectively converted a multi-hour manual session into a largely hands-off computation, while producing spatial patterns of elevation loss and gain that were broadly comparable between the two methods. The authors are careful to note that localized differences appeared at the level of individual points, a reminder that automation changes the numerical details even when the big picture holds steady.

Those spatial patterns themselves tell an interesting story. Across the pipeline corridors of Jefferson County, the differenced LiDAR surfaces revealed a patchwork of elevation loss and gain rather than a uniform regional signal. Some stretches of ground dropped measurably between 2006 and 2017, while adjacent areas rose, reflecting the competing influences of sediment deposition, land development, excavation, vegetation change, and possibly deeper-seated compaction. This spatial variability is precisely why a screening tool is valuable: instead of assuming the entire corridor behaves the same way, operators can use automated change maps to flag specific hotspots where the surface has moved enough to warrant a closer look with field instruments.

The team did not stop at the LiDAR comparison. They also qualitatively cross-checked their results against two entirely independent sources of vertical-motion information: repeated RTK and GNSS survey measurements on geodetic benchmarks in the county, and interferometric synthetic aperture radar, or InSAR, which measures ground deformation from satellites. The authors are refreshingly candid about the limits of this comparison. Because the GNSS, InSAR, and LiDAR datasets differ in acquisition periods, spatial resolution, measurement characteristics, and the locations of their observations, they cannot serve as direct validation of the LiDAR-derived rates. Instead, they function as independent sanity checks, and the broad agreement in the general pattern of motion lends confidence without overstating precision.

Equally notable is what the researchers refuse to claim. Observed elevation losses along the corridors were not interpreted as confirmed land subsidence, because land development, erosion, deposition, and other localized surface modifications can all imprint themselves on a differenced terrain model. A bulldozed pad site, a dredged canal, or a freshly deposited layer of sediment can look, in a LiDAR difference map, remarkably like genuine ground sinking. By framing their output as a screening product rather than a subsidence diagnosis, the team has drawn a scientifically defensible line that many remote-sensing studies blur. The maps identify where to look; they do not, by themselves, explain why the ground moved.

The broader context makes the work timely. Southeast Texas sits on sediments deposited by ancient and modern river deltas, and the northern Gulf of Mexico coast has documented some of the fastest relative subsidence rates in the United States, driven by fluid withdrawal, sediment compaction, and sea-level rise. Previous work by co-author Joseph Kruger and colleagues has used repeated GPS measurements of National Geodetic Survey benchmarks to chart subsidence rates for flood-risk planning in the region, and recent satellite-based vertical land motion studies in Greater Houston have highlighted how infrastructure itself can be monitored from orbit. The Lamar study adds a complementary, high-resolution aerial perspective that can zoom in on the narrow corridors where pipelines actually run, a scale at which regional satellite products and sparse benchmark networks leave gaps.

For pipeline operators, the practical appeal is scalability. A corridor network spanning hundreds of kilometers crosses many LiDAR tiles, and a workflow that requires an analyst to babysit every tile simply does not scale to routine monitoring. A scripted workflow that can be re-run whenever a new survey flies, with consistent parameters and reproducible outputs, turns multi-temporal LiDAR from a one-off research exercise into an operational screening capability. The authors suggest that the automated approach can help prioritize locations for further field investigation and for complementary ground-deformation monitoring, whether that means leveling surveys, GNSS campaigns, or targeted InSAR time-series analysis at flagged hotspots. In an era when coastal infrastructure faces rising seas, intensifying storms, and slow but relentless ground motion, teaching a script to find the sinking spots first may prove one of the quiet but consequential wins of modern geospatial automation.

Subject of Research: Automated multi-temporal LiDAR analysis of surface-elevation changes along pipeline corridors

Article Title: Automated multi-temporal LiDAR analysis of surface-elevation changes along pipeline corridors using ArcGIS Pro and Python

Article References: Ibironke, A., Albalawi, F., Gautam, A., Wu, X., & Kruger, J. (2026). Automated multi-temporal LiDAR analysis of surface-elevation changes along pipeline corridors using ArcGIS Pro and Python. Natural Hazards, 122(21), Article 668. https://doi.org/10.1007/s11069-026-08429-7

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08429-7

Keywords: LiDAR, land subsidence, pipeline monitoring, Python automation, ArcGIS Pro, point-cloud processing, remote sensing, Jefferson County Texas, infrastructure monitoring, InSAR, ground deformation, geospatial analysis

A team led by Jianke Ma and Tingkai Nian of Dalian University of Technology, together with colleagues at the University of Emergency Management, the Institute of Engineering Mechanics of the China Earthquake Administration, and other Chinese institutions, conducted six large-scale physical model tests at normal gravity, so-called 1 g tests, to watch how a normal-fault rupture propagates upward through overlying soil and interacts with a shallow rectangular foundation placed in its path. Unlike centrifuge experiments, which scale gravity upward to reproduce deep soil stresses in miniature, 1 g testing at large model scale preserves realistic stress conditions in a more direct way for shallow soil profiles, and it allows researchers to instrument the soil and the foundation densely enough to track the full evolution of damage from the first microcrack to final collapse-like deformation.

The experimental setup simulated the permanent ground deformation produced by quasi-static offset of bedrock along a normal fault. In other words, the models captured the slow, monotonic dislocation of the fault block that accompanies surface faulting, not the transient shaking and inertial forces of seismic waves. This distinction matters for engineering practice: near a surface rupture, the permanent offset itself is usually the dominant cause of foundation damage, and isolating it in the laboratory lets researchers attribute every measured response, from soil pressure changes to foundation tilt, directly to the propagating rupture rather than to superimposed vibration.

Each test varied a small number of controlling parameters that engineers and geologists know from field experience to be decisive. The team changed the type of overburden soil, using both clayey and sandy cover sequences; the thickness of that overburden; the dip angle of the fault; and the magnitude of the imposed bedrock fault offset. By watching how the rupture zone widened, how ground deformation spread laterally, and how the foundation responded in each configuration, the researchers could map the domain within which a rectangular shallow foundation would be measurably affected by a future surface rupture.

The experiments revealed a clear three-stage progression in how rupture develops through the soil cover. In the first stage, microcrack damage accumulates as the rising fault tip deforms the overburden without yet forming a through-going shear zone. In the second stage, individual cracks propagate, coalesce, and concentrate into a discrete rupture zone that climbs toward the surface, often refracting and branching as it passes through materials of different stiffness. In the third stage, the rupture reaches a stable configuration in which additional bedrock offset widens and intensifies the deformed zone without fundamentally changing its geometry. Recognizing which stage a site-scale model has reached is central to judging how much of the ground surface is genuinely at risk.

Three factors emerged as the primary controls on the final size of the rupture and deformation domain: fault dip, overburden thickness, and soil type. Steeper faults tend to focus deformation into narrower zones, while gentler dips spread the shear and its associated surface warping over a wider swath. Thicker overburden gives the rupture more room to refract and diffuse, generally broadening the zone of visible ground deformation even as it attenuates the sharpness of the offset. Clayey soils, which can sustain cohesive shear bands and tensile cracks, behave differently from sandy soils, in which deformation distributes through dilatant shear zones whose geometry depends strongly on density and confining stress. Because real fault zones cut through layered, heterogeneous cover sequences, the study’s systematic variation of these parameters provides a practical envelope rather than a single universal number.

To translate the measured rupture, deformation, and foundation response characteristics into a usable planning tool, the team established a multi-indicator envelope procedure. Rather than relying on one criterion, such as the visible trace of the surface scarp, the procedure integrates six independent indicators: the internal development of the rupture within the soil, the pattern of ground surface deformation, the variation of soil pressure around the foundation, the tilt of the foundation, the strain measured at the foundation base, and the distribution of contact pressure between the foundation and the soil beneath it. The setback distance is then taken as the envelope that encloses the region in which any of these indicators shows meaningful influence from the rupture. This approach is conservative by design, because it captures effects, such as subtle changes in contact pressure or small foundation rotations, that a purely visual mapping of surface cracks would miss.

Under the adopted similarity relationship, which scales the laboratory measurements up to prototype dimensions, the results give concrete numbers for setback widths. For a 30-meter-thick clayey overburden, the total setback width ranged from 30.0 to 43.5 meters depending on the fault dip and offset conditions tested. Halving the clayey overburden thickness to 15 meters reduced the total setback width to 24.0 meters, reflecting the narrower deformation domain that develops when the rupture has less soil to traverse. For sandy overburden, the total setback width fell between 21.0 and 30.0 meters. These figures apply to rectangular shallow foundations of the type tested and to the normal-faulting configurations examined, but they provide a physically grounded starting point for site-specific assessment in active fault zones.

One of the most consistent findings across all six tests was asymmetry: the setback distance required on the hanging-wall side of the fault was always larger than the distance required on the footwall side. This makes mechanical sense for normal faulting, because the downward-moving hanging wall drags the overlying soil into a wider zone of extension, warping, and subsidence, while the footwall side experiences comparatively localized deformation near the fault trace. For land-use planning, the implication is direct: a symmetric setback band drawn around a mapped normal fault trace will underprotect the hanging-wall side and overconstrain the footwall side. Asymmetric setbacks, calibrated to the fault dip and the soil conditions, use land more efficiently while providing equal margins of safety on both flanks.

The study addresses a well-documented gap in earthquake engineering practice. Regulatory frameworks such as California’s Alquist-Priolo Earthquake Fault Zoning Act and comparable guidelines in Utah and New Zealand rely largely on mapped rupture traces and empirical avoidance distances, while the physical-model literature has concentrated heavily on reverse faulting and on strip foundations in two dimensions. Post-earthquake reconnaissance, including investigations after the 2023 Kahramanmaraş earthquake doublets in Türkiye and earlier events such as the Wenchuan and Yushu earthquakes in China, has repeatedly shown that buildings on or near ruptured faults suffer the most severe and least repairable damage. By providing instrumented, three-dimensional evidence for rectangular foundations interacting with normal-fault ruptures in both clayey and sandy cover soils, and by converting that evidence into quantitative setback distances through a transparent multi-indicator procedure, the researchers have given geotechnical engineers and seismic hazard assessors a firmer experimental footing for one of the oldest questions in fault-zone land use: how close is too close.

Subject of Research: Physical model testing of setback distances for rectangular foundations subjected to earthquake-induced normal-fault surface rupture

Article Title: Setback distances for rectangular foundations subjected to earthquake-induced normal-fault surface rupture: 1 g physical model tests

Article References: Ma, J., Zhang, J., Li, P., Zhang, H., Chen, Y., Wang, T., Chen, H., & Nian, T. (2026). Setback distances for rectangular foundations subjected to earthquake-induced normal-fault surface rupture: 1 g physical model tests. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02656-w

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02656-w

Keywords: normal fault, surface rupture, setback distance, rectangular foundation, physical model tests, overburden soil, fault dip, ground deformation, geotechnical engineering, earthquake hazard, hanging wall, shallow foundation