The research, carried out by Muqit Ajmain Shahriyar of the Department of Geology and Mining at the University of Rajshahi, pits two established techniques against each other across the same landscape. The first is Probabilistic Seismic Hazard Analysis, or PSHA, the quantitative backbone of modern building codes worldwide. PSHA treats earthquakes as a statistical problem: it combines a catalogue of past events, a recurrence relationship describing how often quakes of each size occur, and an equation predicting how strongly the ground will shake at a given distance from a rupture. The second approach is the Analytic Hierarchy Process, or AHP, a multi-criteria decision-making method that weighs contributing factors, such as fault proximity and geological conditions, against one another to produce a susceptibility ranking. Because the two methods rest on entirely different assumptions, agreement between them carries real weight.

The probabilistic half of the study was built on a grid-based framework covering the entire corridor from Sylhet to the Chittagong hill districts. Shahriyar compiled a regional earthquake catalogue and fitted it with a Gutenberg-Richter recurrence analysis, the classic logarithmic relationship that links the frequency of earthquakes to their magnitude. Ground shaking was then estimated using the Ground Motion Prediction Equation developed by Sharma and colleagues in 2009, which draws on strong-motion data from the Himalayan and Zagros regions, tectonic environments broadly comparable to Bangladesh’s. The analysis was run for two standard hazard levels: a 10 percent probability of exceedance in 50 years, corresponding to a return period of roughly 475 years, and a more stringent 2 percent probability, corresponding to roughly 2,475 years, the level typically used for critical infrastructure design.

The numbers that emerged are striking. At the 475-year return period, predicted peak ground acceleration values range from 0.084 g to 1.53 g across the study area, where g is the acceleration due to gravity. At the 2,475-year level, the range widens to between 0.10 g and 1.89 g. For context, shaking above roughly 0.2 g is generally strong enough to cause damage to vulnerable buildings, and values approaching 1 g and beyond imply violent ground motion capable of devastating even well-constructed structures. The upper end of these estimates is concentrated in a specific location: Barkal Upazila in Rangamati District, in the Chittagong Hill Tracts, emerges from the PSHA as the single highest seismic hazard zone in the entire study corridor.

Barkal does not stand alone. The probabilistic maps also flag elevated hazard across surrounding parts of Rangamati, as well as in Chandpur, Cumilla, Sylhet, and Moulavibazar. This spatial pattern reflects the geometry of the underlying fault systems, particularly the active structures of the Tripura Fold Belt and the Chittagong-Tripura Boundary Fault, which thread through the region and accumulate strain as the plates continue their slow collision. The hazard values are not predictions of when an earthquake will strike, but statements about the level of shaking that has a defined probability of being exceeded within a given window of time, a distinction that matters greatly for engineers and planners who must translate the numbers into design requirements.

The second half of the study took a different route to the same question. Using the Analytic Hierarchy Process, first formalized by mathematician Thomas Saaty in the 1970s, the assessment assigned relative weights to the factors that control seismic susceptibility and combined them into a single index for each location. The AHP-based susceptibility map shows a distribution that is broadly comparable to the PSHA result but with subtle differences in where the highest categories fall. In this model, high-susceptibility zones cluster in parts of Sylhet, Sunamganj, Moulavibazar, Feni, Cumilla, and Chittagong. The differences between the two maps are instructive: PSHA is driven primarily by earthquake recurrence and ground-motion physics, while AHP responds to the weighted combination of susceptibility criteria, so each method illuminates aspects of the hazard picture the other may understate.

Where the two approaches overlap is where the findings become most consequential. Sylhet, Moulvibazar, Sunamganj, Chandpur, Cumilla, Rangamati, and Chittagong are all identified as areas of elevated seismic potential by both methods independently. That convergence is significant because these are not empty hinterlands. Sylhet and Chittagong are major urban centers with rapidly growing populations and building stocks of widely varying quality, and the intervening districts contain millions of residents. When two techniques with different theoretical foundations point to the same places, the case for prioritizing those areas in seismic risk mitigation and land-use planning becomes difficult to ignore.

The study arrives at a moment when Bangladesh’s seismic exposure is drawing increasing scientific attention. Previous work has applied the Gutenberg-Richter relationship and spectral analysis to national seismicity, updated probabilistic hazard assessments for the country as a whole, and used GIS-based AHP methods to assess earthquake risk in other tectonically active regions, such as Bitlis Province in Türkiye. The comparative approach taken here adds a layer of robustness that single-method studies cannot provide. It also highlights a practical challenge for a country whose building codes have been revised to address geotechnical earthquake engineering concerns: hazard maps are only useful if they are trusted, and demonstrating that independent methods converge on the same high-risk zones helps build that trust among the officials who must act on them.

For residents of the region, the practical message is that the earthquake threat is not evenly distributed, and the areas of greatest concern are now mapped with two independent lines of evidence. The highest modeled shaking, approaching 1.9 g in the most extreme scenario, is centered on the hill tracts of Rangamati, while a broad band of elevated hazard runs through the Sylhet region and the central districts along the fold belt. Translating these hazard levels into safer communities will require region-specific mitigation measures, from enforcing seismic design provisions in new construction to retrofitting vulnerable buildings and steering future development away from the most susceptible ground. The study’s author notes that the data generated and analyzed are available from the corresponding author upon reasonable request, and the work was carried out with academic support from the Department of Geology and Mining at the University of Rajshahi, without external funding. As the plates beneath Bangladesh continue their inexorable convergence, maps like these, tested against each other and grounded in decades of seismological theory, offer one of the few tools available for looking ahead to the next great earthquake before it arrives.

Subject of Research: Comparative seismic hazard and susceptibility mapping in northeastern to southeastern Bangladesh

Article Title: Comparative seismic hazard assessment using PSHA and AHP-based susceptibility mapping in northeastern to southeastern Bangladesh

Article References: Shahriyar, M. A. (2026). Comparative seismic hazard assessment using PSHA and AHP-based susceptibility mapping in northeastern to southeastern Bangladesh. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02672-w

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02672-w

Keywords: seismic hazard, PSHA, Analytic Hierarchy Process, Bangladesh, peak ground acceleration, Gutenberg-Richter, ground motion prediction, Dauki Fault, Chittagong-Tripura Boundary Fault, Sylhet, Rangamati, earthquake risk

The core insight is that every building acts as a mechanical filter sitting between the earth and its occupants. When seismic waves arrive at a foundation, the structure amplifies, attenuates, and delays them according to its own dynamic properties, so the acceleration recorded on an upper floor is not the ground motion itself but a transformed version of it. For decades, engineers have treated this transformation as a nuisance to be corrected for. The new work inverts that logic: if the filtering behavior of a building can be modeled accurately enough, then the building’s own response becomes a rich source of information about the shaking it experienced at its base. In effect, the city’s building stock becomes a distributed, already-installed seismic sensing array.

Technically, the challenge is an inverse problem: given the acceleration time histories measured by structural response sensors, recover the peak ground acceleration, peak ground velocity, and peak ground displacement at the building’s location. The researchers begin by processing the raw structural acceleration records to extract multiple response parameters, which are then fused together to estimate the three ground-motion intensity measures. Fusing several parameters rather than relying on a single feature gives the learning algorithm a more robust signature of the underlying ground motion, because different parameters carry complementary information about amplitude, frequency content, and duration of the shaking.

To make the framework work at the scale of an entire city, the study area is discretized into grid cells, and within each cell a representative building is modeled as a nonlinear multi-degree-of-freedom shear structure. This is a standard idealization in earthquake engineering in which each story of the building is represented by a lumped mass connected to its neighbors by nonlinear springs, capturing how the structure yields and deforms under strong shaking. By simulating earthquakes acting on these representative buildings, the team generates the training data needed to teach a model how structural responses map back to ground motions for each building type, without waiting decades to accumulate real paired observations of ground truth and building response.

The machine learning architecture at the heart of the method, called ECA-Deep Net, is a hybrid framework that combines convolutional neural networks with long short-term memory networks, and it is trained separately for each representative building. The convolutional layers excel at extracting local features from the sensor waveforms, while the LSTM layers capture the temporal dependencies that matter in a seismic record, where the ordering and persistence of pulses carry physical meaning about the wavefield. A distinctive ingredient is the efficient channel attention mechanism, borrowed from the computer vision architecture ECA-Net. Channel attention allows the network to learn which feature channels are most informative for the task and to weight them accordingly, using a lightweight mechanism that adds almost no parameter overhead. In this application, attention helps the model decide which aspects of the multi-sensor structural response deserve emphasis when inferring ground-motion intensity.

The city-scale validation used a simulated earthquake, comparing the ground-motion maps generated by the framework against reference ground-motion fields computed by conventional means. The results showed that the predicted maps effectively reproduce the spatial variability of the reference fields, which is a critical property: ground shaking in real earthquakes varies sharply over short distances because of soil conditions, basin geometry, and source directivity, and a mapping method that smooths away that variability would mislead emergency responders about which neighborhoods were hit hardest. The framework also proved stable under varying levels of sensor noise at city scale, an essential robustness test for a method intended to run on low-cost hardware in real buildings rather than laboratory-grade instruments.

Perhaps the most compelling evidence comes from the field. The team validated the approach using recorded responses from an instrumented six-story real building, testing the models on earthquake records the network had never seen before, without any architectural modification or hyper-parameter retuning. The models achieved correlation coefficients of up to 0.97 between predicted and actual ground-motion parameters, with error values ranging from 7.33 percent to 12.05 percent. That level of transfer performance, on unseen events and with no recalibration, suggests the learned mapping is capturing genuine physics of the building-ground interaction rather than overfitting to the training set.

The practical implications reach well beyond seismology. Rapid ground-motion maps feed directly into shake maps, damage estimation, and emergency routing decisions, and the cost barrier of conventional strong-motion networks is one of the main reasons many earthquake-prone cities in developing regions lack adequate coverage. Structural health monitoring sensors are increasingly being installed in buildings anyway, for condition assessment and safety management, so a method that doubles them as ground-motion sensors offers a scalable, data-driven, and rapidly updatable alternative. Because each model is tied to a specific representative building, the network can grow incrementally: as more instrumented buildings come online, more grid cells gain coverage, and the city map fills in organically.

The work also fits into a broader movement in earthquake science toward machine learning-based ground motion modeling, from neural network prediction of peak ground acceleration to deep learning approaches using single-station waveforms and volunteer-hosted MEMS accelerometer networks. What distinguishes this study is the explicit use of the building as the sensing element, with the inversion handled by a building-specific model rather than a one-size-fits-all network. The authors acknowledge that the approach relies on representative structural models and simulated training scenarios, and the datasets and code files will be made available on request, which should allow other research groups to test the framework on their own instrumented buildings and urban grids.

If the approach matures from simulation and single-building validation into operational deployment, the aftermath of the next major urban earthquake could look very different. Within minutes of the shaking stopping, emergency managers could have a continuously updated, block-by-block picture of ground-motion intensity assembled from the very structures the earthquake was trying to destroy, informing search-and-rescue priorities, inspection queues, and utility shutdown decisions. The buildings that shelter a city, the study suggests, can also become the instruments that watch over it, provided the deep learning models translating their swaying into seismology are built with the care that this new framework demonstrates.

Subject of Research: City-scale seismic ground motion mapping using building-embedded structural response sensors and hybrid deep learning

Article Title: City-scale ground motion mapping from building-embedded structural response sensors using building-specific hybrid deep learning models

Article References: Zar, A., Li, S., Li, C., Li, C., & Zhao, J. (2026). City-scale ground motion mapping from building-embedded structural response sensors using building-specific hybrid deep learning models. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02659-7

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02659-7

Keywords: ground motion mapping, structural response sensors, deep learning, earthquake engineering, channel attention, LSTM networks, convolutional neural networks, peak ground acceleration, seismic monitoring, structural health monitoring, shear building model, sensor data fusion

The research, led by Manoharan Sambath and Sembulichampalayam Sennimalai Chandrasekaran of Vellore Institute of Technology together with Sandeep Maithani of the Indian Institute of Remote Sensing, forms part of a broader effort to assess urban seismic risk in India, a country where rapid construction often outpaces detailed geotechnical knowledge. Funded by the Indian Space Research Organisation through its RESPOND program, the study combined borehole records, geophysical surveys and computational modeling to produce some of the most granular ground-motion estimates yet available for the city.

At the heart of the work lies a technique called Site-Specific Seismic Ground Response Analysis, or SSGRA. The method takes an earthquake signal defined at bedrock depth and propagates it upward through the local soil column, accounting for how each layer stiffens, softens and dissipates energy as it is deformed. Because soil is not a simple elastic medium, the team ran two parallel formulations: an equivalent linear approach, which uses averaged stiffness and damping values, and a more computationally demanding nonlinear analysis that tracks the true stress-strain behavior of the sediments under strong cyclic loading.

The input side of the calculation was equally rigorous. The researchers drew on a probabilistic seismic hazard analysis of the Coimbatore region, which they had previously developed using a logic-tree framework, to define five distinct hazard zones based on bedrock peak ground acceleration. For each zone, they constructed a Uniform Hazard Response Spectrum and then spectrally matched eleven suitable recorded ground motions to it, ensuring that the input signals carried the right energy content across the frequency range that matters for buildings. These matched motions were then applied for two hazard levels, corresponding to return periods of 475 and 2475 years, roughly the shaking expected once in 475 and once in 2475 years respectively.

Characterizing the soils themselves required fieldwork across the city. The team used 40 geospatially distributed bore logs, which record the layering and index properties of the subsurface, together with active Multichannel Analysis of Surface Waves tests. MASW surveys measure how seismic surface waves disperse through the ground, allowing engineers to compute the average shear-wave velocity in the top 30 meters, a parameter known as Vs30 that has become the international standard for classifying site conditions. Soils with low Vs30 values, typically soft clays and loose sands, amplify ground motion far more than dense gravels or rock.

The results paint a strikingly uneven picture of Coimbatore’s seismic vulnerability. The neighborhoods of Rathinapuri, Avinashi, Gandhipuram and NGGO Colony showed a broad variation in surface peak ground acceleration, meaning that even within these areas, the intensity of shaking could differ substantially from one site to the next depending on local soil conditions. Such block-by-block variability has direct consequences for building codes, which often assume a single design value across an entire city.

Perhaps the most consequential finding concerns spectral acceleration, the quantity that most directly governs structural design. The central part of the city exhibits the highest surface spectral accelerations at spectral periods of 0.5 and 1.0 seconds, ranges that coincide with the natural vibration periods of many mid-rise reinforced concrete buildings. In an earthquake, structures resonate with ground motion at their own natural periods, so elevated spectral acceleration in this band implies that a significant share of Coimbatore’s building stock could experience forces well above what a uniform hazard map would suggest.

The study also found a clear geographic pattern in amplification, the ratio of surface motion to bedrock motion. The western region of the city experienced higher amplification factors for the 475-year return period event, indicating that its subsurface conditions selectively boost moderate-intensity shaking. Interestingly, the relationship between amplification and shaking intensity is not constant: as input motions grow stronger, soils soften and damp more, which is precisely why the team ran both linear-equivalent and nonlinear analyses across the two return periods rather than relying on a single scenario.

The comparison with codified design spectra carries a cautionary message for practitioners. The site-specific Acceleration Design Response Spectrum proposed in the study was found to be overestimated, particularly for the second group of bedrock PGA-based hazard zones, when set against the nonlinear surface response spectra computed by the analysis. In other words, for those hazard zones, the design spectrum is conservative, demanding more seismic resistance than the physics of the soil column requires. While conservatism is not inherently dangerous, it imposes unnecessary construction costs; the flip side is that in zones where the spectrum is not conservative, the margin of safety may be thinner than assumed.

For Coimbatore, a city whose economy is anchored by textile manufacturing, engineering industries and a dense fabric of both formal and informal construction, the maps produced by this study offer a practical template for risk-sensitive planning. The work also demonstrates a methodology that other Indian cities can replicate: combine probabilistic hazard analysis with dense geotechnical sampling, geophysical profiling and dual-formulation site response modeling to move from city-wide averages to neighborhood-level truth. As India’s seismic monitoring network expands and urban populations grow, studies of this kind convert abstract hazard curves into concrete guidance about where the ground will shake hardest, and where engineers should look most carefully before the next earthquake tests their assumptions.

Subject of Research: Site-specific seismic ground response analysis and geospatial mapping of surface ground motion parameters for Coimbatore city, India

Article Title: Geospatial variation of surface ground motion parameters using the 1-D Site-Specific Seismic Ground Response Analysis (SSGRA) for Coimbatore city, Tamil Nadu, India

Article References: Geospatial variation of surface ground motion parameters using the 1-D Site-Specific Seismic Ground Response Analysis (SSGRA) for Coimbatore city, Tamil Nadu, India. (n.d.). https://doi.org/10.1007/s10518-026-02705-4

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02705-4

Keywords: seismic hazard, ground response analysis, Coimbatore, shear-wave velocity, MASW, peak ground acceleration, spectral acceleration, site amplification, soil dynamics, earthquake engineering, India, borehole data