Earthquake and tsunami disasters occur suddenly, and their effects are widespread. To respond effectively to such disasters, it is important to quickly gain an overall understanding of the damage. However, information about earthquakes or tsunamis, as well as damage to buildings and infrastructure, is typically gathered separately through visual inspection and manual processes. By leveraging modern sensor technology, a SATREPS project titled “Development of Integrated Expert System for Estimation and Observation of Damage Level of Infrastructure in Lima Metropolitan Area” was launched in 2021. The project aims to develop a system that provides a comprehensive picture of the damage immediately following a disaster, integrating the findings into a geographical information system with display capabilities. Group 1 focuses on earthquake and tsunami hazards, estimating the level of shaking and predicting tsunami damage. Group 2 focuses on damage detection and estimation, using sensors to assess the extent of damage to buildings and other infrastructure. Group 3 uses satellite imagery to understand the damage over a wide area and integrates the data from Groups 1 through 3. Group 4 is dedicated to developing the capacity to use the integrated expert system. The project is set to conclude in September 2026. This special issue has been organized to present the progress made over the past four years and to share the project’s achievements. It addresses earthquake hazards, damage estimation for buildings and infrastructure, satellite imagery for damage assessment, and human loss estimation.

Eight shear wave velocity profiles of deep sedimentary layers in Lima, Peru, were determined using seismic interferometry. To achieve this objective, seven high-sensitivity sensors monitoring continuous ground motion were installed. Sensor data allowed the extraction of Green’s functions through cross-correlation analysis in the frequency domain. It was determined that continuous recording data with a minimum of 2,000 h (equivalent to three months) is necessary to obtain a Green’s function approximation. Dispersion curves of surfaces from 0.3 to 2 s were obtained from maximum values of Green’s functions between two stations after applying Gaussian filters. Thus, obtaining deep soil profiles through the inversion of dispersion curves extended to periods of around 1 s is possible. Finally, the estimated profiles were verified by comparing their theoretical ellipticity curves with H/V spectral ratios of microtremors, finding a good match for periods up to 1 s. These profiles reach depths and S-wave velocities of up to 600 m and 3,000 m/s, respectively.

The seismic response evaluation of soil deposits in Metropolitan Lima requires analysis methods that can capture local amplification effects, owing to the region’s complex alluvial stratigraphy and high seismicity. This study performs a comparative assessment of four site response analysis methods using records from the 2021 Mala (M_w 6.0) earthquake, located south of Lima: (1) normative amplification factors (NAF E.030), (2) ground motion models (GMM), (3) average amplification factors (AAF), and (4) one-dimensional wave propagation analysis (1DPA). Performance was evaluated using root mean square error (RMSE) between observed and predicted response spectra as the primary accuracy metric. A total of 31 accelerographic stations were analyzed, classified by time-averaged shear wave velocity of the top 30 m soil layers (V_s30) into three categories: stiff (S1’: 550–800 m/s), intermediate (S2^′: 350–550 m/s), and soft (S3’: 200–350 m/s) soils. Linear elastic soil behavior was confirmed through 1DPA, indicating maximum shear strains below 0.01% across all sites, well within the elastic threshold. The results revealed that 1DPA analysis produced the most accurate estimates, with average RMSE values of 27.4 cm/s² for S1’ soils and 38.5 cm/s² for S2’ soils. NAF E.030 performed well in 8 out of 10 stiff soil sites (RMSE =33.1 cm/s²), however, exhibited significant limitations in softer soils where RMSE values exceeded 37 cm/s². GMM and AAF methods demonstrated RMSE values ranging from 44.8 to 51.4 cm/s², with reduced accuracy in long-period ranges (>1.0 s) and S3’ soil sites. These findings highlight the need to revise current normative amplification factors for soft soils and support the implementation of 1DPA analysis in areas with complex stratigraphy or deep sedimentary deposits, thereby contributing to improved seismic-resistant design practices in Metropolitan Lima.

The impact of natural hazards can be effectively mitigated through preliminary assessments that combine geospatial and modeling approaches. Among these, tsunami exposure mapping plays a crucial role in coastal risk evaluation. This study develops a methodological framework for tsunami exposure assessment by integrating numerical simulation and geospatial analysis. Two tsunamigenic seismic scenarios were considered: (i) a historically documented event and (ii) a plausible large-magnitude earthquake off the Pacific coast of Lima, Peru. Tsunami propagation and inundation were simulated using the TUNAMI-N2 model to generate detailed inundation maps that delineate the spatial extent and inundation depth. The temporal evolution of tsunami impact distribution was analyzed using arrival-time maps and synthetic tide gauges, strategically located along the coastline to record wave amplitude variations over time. The population exposed to tsunami hazard was quantified using national census demographic data, while critical buildings were identified from official geospatial databases. This integration provides refined insights into tsunami hazard dynamics and supports evidence-based coastal risk management and urban growth.

Peru faces a significant tsunami hazard due to its location along the Pacific Ring of Fire. Historical megathrust earthquakes and their resulting tsunamis have caused severe damage, highlighting the need for improved warning systems. This study investigates potential tsunami impacts along the central Peruvian coast—including the regions of Ica, Callao, Lima, and Ancash—using numerical simulations. To enable rapid and efficient simulations, we developed gWave-CPU, a parallelized version of the TUNAMI-N2 model created at Tohoku University. Using 90-m-resolution topographic and bathymetric data in combination with census data, we assessed population exposure under two seismic scenarios: a plausible event based on interseismic coupling and a historical scenario simulating the 1746 tsunami. Under the historical scenario, the exposed population was estimated at 320,128, with the highest concentrations in Callao and Lima. Numerical simulations of four hours of tsunami propagation and inundation were conducted using our parallelized implementation, reducing computation time to 68 minutes—a 26.3-fold speedup compared to the conventional model. The results demonstrate that tsunami inundation and population exposure in this region can be efficiently estimated using the proposed approach, providing a valuable contribution to tsunami hazard assessment, management, and emergency preparedness along Peru’s central coast.

Tsunami risk-reduction measures must consider the full range of possible disaster outcomes and their probability of occurrence. This study conducted a probabilistic tsunami hazard assessment (PTHA) for the Chorrillos District using a probabilistic model that accounts for the time and space interactions of earthquake mainshocks. A total of 433 scenarios in the central Peruvian subduction zone within the range of M_w 7.5–9.0 were considered to develop tsunami hazard curves over the next 50 years and tsunami hazard maps for return periods of 475 and 2475 years. We further combined the tsunami hazard results and empirical tsunami fragility functions to assess building damage while accounting for construction materials. A time-independent analysis was implemented and compared with the spatiotemporal model to assess the influence of the seismic gap on tsunami hazards. The results revealed that the spatiotemporal model successfully captures the influence of seismic gaps on tsunami hazards. However, the time-independent analysis produces a higher tsunami hazard and greater building damage than the spatiotemporal model. This study is the first application of PTHA along the central Peruvian coast and can be implemented in tsunami hazard assessments for local communities across the northern, central, and southern regions of the Peruvian coastline.

This paper presents a method for identifying the damping coefficient during earthquakes in quasi-real time using the information from sensors installed in real structures. The method uses a given number of points to perform a quadratic regression in such a way as to reduce the error in identification. A coefficient of determination is also used to discard values that have not been correctly identified. The validity of the method is assessed by dynamic analysis in single-degree-of-freedom systems, using the initial stiffness proportional damping model and the tangent stiffness proportional damping model. Good identification results are obtained using a data acquisition sampling frequency of 200 Hz. The same is true for using a sampling frequency of 100 Hz as long as the system period is greater than 0.40 s. Additionally, it is observed that a 20% identification error does not substantially affect the seismic response of the system. The method is also applied to results from shake table tests, and their identified values are analyzed. A comparison is made between the behavior of the damping coefficient observed in the experimental tests with the behavior assumed in the dynamic analysis. In general terms, it is observed that the damping coefficient does not present abrupt changes in its values when changing the instantaneous stiffness as occurs in the tangent stiffness proportional damping model. Additional conclusions are presented that allow us to continue expanding the knowledge on the damping coefficient behavior during earthquakes.

Between 1997 and 2013, low-ductility reinforced concrete (RC) wall systems were widely used in Peru’s real estate sector due to their economic advantages. Despite good engineering practice recommending a five-story height limit, this restriction was not explicitly included in the Peruvian standards of that period. Regulatory provisions were often supported by numerical models calibrated using one-story wall specimens, which neglect the flexural behavior that dominates in medium-rise structures. This underscores the need to evaluate the inelastic flexural response of such buildings under seismic loading. Cyclic loading tests were conducted on a half-scale, five-story RC wall to calibrate numerical models using a nonlinear multi-spring model of concrete and steel reinforcement behavior. The calibrated model enables accurate assessment of damage progression and performance under varying seismic intensities. Findings reveal that buildings constructed with low-ductility RC walls may have overestimated seismic capacity in their original design. Under severe seismic events, these buildings are likely to exceed reparability thresholds, while under rare seismic events, they approach structural collapse.

Seismic risk assessment is essential for reducing earthquake-related damage in urban areas. Accurate recognition of building features is a key factor in evaluating seismic vulnerability, yet traditional manual inspection methods are inefficient and prone to error. This study proposes a deep learning-based framework using convolutional neural networks for automated instance segmentation of building features to support seismic risk estimation in Lima, Peru, a seismically active region with diverse architectural styles. Leveraging state-of-the-art models like Mask R-CNN, the framework identifies and segments structural components such as walls, windows, and columns from building imagery. By integrating geospatial data and remote sensing technologies, the proposed approach enhances seismic risk evaluations through automated, scalable, and precise feature recognition. Despite challenges such as occlusions, varying lighting conditions, and architectural diversity, the model aims to adapt through specialized training tailored to Lima’s urban landscape, contributing to more efficient disaster preparedness and response in earthquake-prone regions.

The metropolitan area of Lima and Callao located on the central coast of Peru, experiences high seismicity. However, the effects on critical infrastructure, particularly the urban road network, have been insufficiently studied. This study aims to evaluate two indexes using a matrix database, organized into four categories with 17 evaluation parameters, within a representative study area that includes all soil types defined by Peruvian regulations. First, data were collected to parameterize the main factors for determining seismic vulnerability indexes, which identify the most vulnerable sectors or roads. A second index of operational functionality assesses the road’s importance and its performance during peak and off-peak hours to identify which roads would remain operational in disaster. This selection prioritizes roads with greater capacity to connect main facilities, support urban logistics and facilitate passenger transport, particularly in areas with major mass transportation systems and the national highway that transports goods to the north, center, and south of the country.

Effective post-earthquake decision-making requires reliable information regarding the structural damage to infrastructure. Service stations—commonly known as gas stations—represent critical infrastructure whose failure can have severe consequences in urban areas, including fires, casualties, property damage, and health risks. This study investigates the seismic behavior of such structures using a 1:10 scaled model tested on the shaking table at Peruvian-Japanese Center for Seismic Research and Disaster Mitigation. The experimental response was analyzed using signal processing techniques and modal identification within a bandpass frequency range of 1 to 5 Hz. Based on similitude laws, an equivalent full-scale dynamic model was calibrated to represent the real structure’s response. Subsequently, following SEAOC recommendations and the Peruvian Seismic Design Standard (NTE E.030), 150 capacity analyses were performed on service station models, considering structural height as the primary variable. For each height, force–drift relationships were derived, and corresponding peak ground accelerations were calculated for deterministic seismic events. The resulting damage intervals provide quantitative thresholds for evaluating the seismic performance of service stations. These outcomes can be incorporated into Sistema Experto Integrado para la Evaluación de Daños por Sismo, an expert system for seismic damage assessment in urban environments.

Critical infrastructure such as water tanks, bridges, and similar structures must remain operational after a major earthquake to support emergency response efforts and facilitate the recovery of social and economic activities. With its high population density and significant seismic risk, Metropolitan Lima contains a large proportion of such vulnerable infrastructure. However, the seismic vulnerability of these structures has not been extensively examined due to limited data availability, accessibility challenges, and other constraints. Developing methodologies tailored to Peruvian infrastructure to assess seismic reliability is therefore essential to address this gap. This study proposes a methodology for deriving fragility functions for elevated water tanks and vehicular bridges. Although the fragility functions are based on empirical models, the parameters and applications are adapted to Peruvian conditions. The research was conducted as part of the SATREPS project, Development of Integrated Expert System for Estimation and Observation of Damage Level of Infrastructure in Lima Metropolitan Area, led by the Japan–Peru Center for Earthquake Engineering Research and Disaster Mitigation at the Faculty of Civil Engineering, National University of Engineering.

Lima City and Callao Province are located in the Pacific Ring of Fire and have experienced major earthquakes over the years. To understand the seismic response of the soil, a seismic microzonation study was developed, taking into account the results of geological, geotechnical, and geophysical surveys. The geological study identified areas with potential for rockfall on the existing hills in Lima. The geotechnical exploration included survey drilling, soil sampling for analysis, and standard penetration test. The results defined the mechanical characteristics of the soils and identified their strength and behavior as foundation materials. The geophysical exploration consisted of single-point microtremor measurements, microtremor arrays, MASW (multichannel analysis of surface waves), and refraction tests. The results provided the natural period of vibration and the velocity profiles of the P and S waves, allowing the dynamic characteristics of the soils to be identified. The results of the geological, geotechnical, and geophysical surveys were superimposed to develop the geotechnical microzonation map, the isoperiod map, and the seismic microzonation map for Lima City and the Callao Province. The latter identifies five zones, where the area with the best mechanical and dynamic soil characteristics covers most of the study area.

Estimating building heights is essential for urban planning, building resilience, and disaster risk assessment. This study introduces a method for deriving building heights using stereo pairs of high-resolution optical satellite images. A 3D point cloud was generated through stereo matching of two optical satellite images, and a high-resolution digital surface model was constructed to estimate building heights. In the absence of detailed building outlines, building height was calculated by subtracting the elevation outside the lot outline from that within the lot outline. Estimated building heights were then converted to floor numbers for evaluation. Validation involved comparing estimated floor numbers with manually counted floor numbers from field surveys and a 3D model generated from UAV imagery. Results indicated that 42% of buildings had accurately estimated floor numbers, and 82% were within a one-floor error margin. This approach offers a rapid, cost-effective solution for height estimation, especially in areas lacking detailed cadastral or LiDAR data.

Early damage estimation in the immediate aftermath of a catastrophic earthquake is critical for effective disaster response and informed decision-making, especially in densely populated urban areas with high seismic activity, such as the Lima Metropolitan Area, Peru. This research focuses on designing and developing a web-based geographic information system for early earthquake damage estimation, featuring a four-stage workflow. Stage 0 generates pre-event damage estimations using earthquake scenarios, infrastructure datasets, and vulnerability models. Stage 1, initiated immediately after an earthquake, selects the closest pre-calculated scenario by comparing the peak ground acceleration values recorded by accelerometers with pre-estimated intensity measures. Stage 2 creates ShakeMaps by interpolating observed ground motion indices to produce the updated damage estimates based on real-time intensity distributions. A future Stage 3 will refine these estimates using post-event observed data. Data acquisition relies on open geospatial services and Peruvian governmental platforms, integrating datasets related to general buildings, essential buildings (schools and hospitals), transportation networks (bridges and roads), and lifeline systems (water supply and sewage). The system was implemented using open-source geospatial tools, providing a cost-effective, scalable solution for early-time disaster management in earthquake regions.

This study examined a community-based disaster risk reduction initiative in a rural area of Sai Yok District, Kanchanaburi Province, Thailand. The study focused on the village’s proactive strategies to address natural hazards such as drought, flooding, and extreme weather conditions. It highlighted the importance of local participation, comprehensive risk assessments, and tailored preparedness programs in reducing the community’s vulnerability. The project illustrated how local communities can enhance their resilience through education, collaboration with governmental and non-governmental organizations, and strategic disaster risk planning. The findings revealed that a well-organized, community-based approach can significantly mitigate the impact of natural disasters, leading to improved economic stability and public health outcomes.

It is empirically known that debris flows are more likely to occur during and after volcanic eruptions. This study conducted numerical simulations to examine the effects of reduced infiltration capacity and increased sediment supply owing to volcanic activity on the rainfall threshold that causes debris flows. Rainfall runoff analysis was performed using various rainfall intensities as constant input conditions. The occurrence of debris flows was evaluated based on theoretical and experimental formulas. The mechanism by which sediment in river channels transforms into debris flow was discussed in terms of two processes: sediment sliding as a mass and sediment being eroded by surface flow. This study clarified the critical role of infiltration capacity in debris flow occurrence. When infiltration capacity is low, surface runoff becomes more dominant, easily entraining sediment and triggering debris flows—even during short or weak rainfall events. Quantitative analysis revealed that when infiltration capacity drops from 100 mm/h to 10 mm/h, the cumulative rainfall required to trigger debris flows decreases to only a few percent of the pre-eruption requirement. However, when volcanic activity supplies large volumes of sediment to river channels, the mechanism can shift to mass sliding. In such cases, debris flows may occur even under relatively weak but prolonged rainfall. The results indicate the importance of both sediment erosion and mass sliding processes in debris flow occurrence.

This study investigates flow resistance models for debris flow simulations based on 2D shallow water equations. With an aim of reproducing the catastrophic debris flow disaster that occurred in Atami City, Japan, in 2021, three flow resistance models are evaluated to capture the flow and deposition characteristics of debris flows. A preliminary parameter study is conducted to calibrate two critical empirical parameters: Manning roughness coefficient and the internal friction angle, by comparing simulation results with experimental data reported in the literature. The calibrated models and their parameter combinations are then subsequently applied to the actual debris flow disaster in Atami City. Their applicability is discussed through comparison of simulation results with post-disaster survey data. The findings indicate that the combined turbulent and frictional resistance model demonstrates superior performance in replicating both the flow and deposition characteristics of the debris flow. However, further refinement is required to fully account for the complex interactions observed during the actual event. Additionally, this study provides insights into valid parameter ranges for specific flow resistance models, contributing to more intuitive debris flow simulations.

Efficiently assembling hospital staff during a large-scale disaster is necessary to prevent hospital dysfunction. Confirming the willingness of employees to assemble and participate is crucial for formulating a viable business continuity plan (BCP). This study investigated the factors associated with hospital staff’s willingness to participate in hospital work during a large-scale disaster. In 2023, a survey was conducted with the staff of Hospital A (n=905; response rate 58.6%), located in a high-risk earthquake zone. Questionnaire items included participant characteristics, willingness to participate in the event of a disaster, reasons for low willingness, and anxiety levels regarding continued work during a hospital disaster. Logistic regression models were used to evaluate the associated factors. Among the participants, 331 (36.6%) reported high willingness to participate in hospital work in the event of a disaster. Factors associated with low willingness included childcare or elderly care responsibilities, lack of financial guarantees, and higher anxiety levels regarding disaster work. Conversely, full-time employment and a commute time of less than 30 minutes were factors associated with higher willingness. These findings confirm the low willingness of hospital staff to participate in disaster work immediately after a disaster. Welfare benefits addressing childcare and elderly care, financial security, and clarification of roles and working hours can increase staff willingness to participate during a disaster. Hospital managers need to develop appropriate BCPs to ensure the availability of hospital staff for disaster management.

Wildfires have been increasing in frequency, intensity and scale. Aerial firefighting is crucial in the support of ground crews to suppress and extinguish the fires. Recent technological, legislative and operational advances have allowed for the introduction of uncrewed aircraft systems (UAS) as a valuable addition to fixed- and rotor-wing crewed aircraft. While UAS can help with the reconnaissance, collisions with helicopters have already been reported. The current research considers the airspace integration of crewed and uncrewed aircraft in disaster response missions. A heli-bucket water drop and refill operations are modeled and tested using an experimental helicopter in five flight tests in the vicinity of Mt. Fuji, Japan. UAS operation volumes are simulated in the vicinity of the helicopter flight area and alerts to aid the pilot’s situation awareness are tested. The results showed that while such situation awareness support increases safety and efficiency of crewed aircraft operations, improvements in the UAS operation volume design can help pilots navigate in less-known and dynamically changing environment.

On December 18, 2022, heavy snowfall in Sado City, Niigata Prefecture, caused widespread tree fall and bamboo collapse, and subsequent power outages. The total number of households affected by the outage reached 17,510 during the event, and restoration of the power supply required approximately 11 days. We conducted field surveys to investigate the characteristics of damage to trees and bamboo, as well as the associated power outages. This survey was conducted using both visual inspections and a vehicle equipped with an artificial intelligence-based road surface assessment system that employed a smartphone camera to document the damage to trees and bamboo and condition of road surface. Additionally, meteorological data and power outage records were analyzed to clarify the detailed weather conditions during the outage events and consider their relationship with the field survey. The results revealed that stem breakage of trees predominantly occurred in mountainous areas at relatively high elevation, whereas bamboo collapse was primarily observed in lowland areas. Analyzing meteorological data and outage records indicated that persistent strong northwesterly winds and intermittent snowfall contributed to snow accretion on trees, leading to uneven snow loading and increased susceptibility to wind damage. These conditions likely triggered widespread damage to trees and bamboo and ultimately resulted in power outages. Furthermore, the presence of dense bamboo stands and dead bamboo adjacent to power lines, which are particularly vulnerable to snow and wind damage, was considered to have contributed to the extensive power outages observed in the area.