Field Data Research Articles

A study of the response of reinforced concrete frames with and without masonry infill walls to earthquake motions

In developing countries prone to frequent earthquakes, it is common practice to use masonry infill walls as partitions in reinforced concrete frame buildings. Frames with infill have smaller drift demands and smaller drift capacities relative to similar frames without infill. Whether the relative reduction in drift demand offsets the plausible reduction in drift capacity remains open to discussion. This investigation provides experimental evidence on drift demands and drift capacities for both frames with and frames without infill. To make fair comparisons, only experiments where sets of nominally identical reinforced concrete frames, at least one frame with no infill and at least one frame with infill, are considered. Because many static experiments provide evidence for the reduction in drift capacity caused by infill, the objective of the dynamic experiments described in this study is to quantify the reduction in drift demand attributed to infill. Results of dynamic tests show that frames with infill drifted no more than one-third the amount similar frames without infill drifted in simulated base motions of the same intensity. Results of static tests show that the smallest drift capacities of frames with infill were approximately one-half of the drift capacities of similar frames without infill. These two experimental observations indicate that infill reduces drift demand more than it reduces drift capacity suggesting an overall beneficial effect of the infill. Field data were used to test this idea. Based on surveys of low-rise school buildings, RC frames with more full-height infill walls tend to have less earthquake damage than frames with less infill. A convenient method to quantify the amount of infill walls in buildings is computing wall ratios defined as the ratio of cross-sectional area of masonry infill walls on ground floor to total floor area above ground floor. Projections of the field data suggest that infill wall ratios of at least 0.5% in both directions of a building reduce the likelihood of severe structural damage by 300% compared with smaller ratios. The use of infill presents an inexpensive yet effective retrofit technique, even if only for temporary use, for strengthening buildings vulnerable to earthquake damage.

Local near-field scattering data enables unique reconstruction of rough electric potentials

Abstract The focus of this paper is the study of the inverse point-source scattering problem, specifically in relation to a certain class of electric potentials. Our research provides a novel uniqueness result for the inverse problem with local data, obtained from the near field pattern. Our work improves the work of Caro and Garcia, who investigated both the direct problem and the inverse problem with global near field data for critically singular and δ-shell potentials. The primary contribution of our research is the introduction of a Runge approximation result for the near field data on the scattering problem which, in combination with an interior regularity argument, enables us to establish a uniqueness result for the inverse problem with local data. Additionaly, we manage to consider a slightly wider class of potentials.

Impact Assessment and Damage Fee Development for Pavement Utility Cuts Using Functional and Structural Field Data

The objectives of this study were: 1) to provide transparency of a recent pavement utility cut impact study which informed the adoption of a pavement utility cut fee in Davis, California; and 2) to present an additional methodology for assessing pavement damage using field data referenced to pavement condition. A total of 24 test sites were selected to represent all functional classifications across the Pavement Condition Index (PCI) spectrum. Functional and structural field data were collected and analyzed. Of the 24 test sites, 67% exhibited both functional and structural damage with 96% exhibiting at least one form of damage. The average PCI reduction resulting from utility cuts was 5.1 points and the corresponding percent reduction in pavement service life was 6%. Of the test sites, 63% exhibited damage 2 ft from the edge of the patch, thus indicating that the zone of influence extends past the current restoration area. The statistical analyses indicated that the pavement utility cuts caused significant damage and supported a tiered utility cut fee structure based on functional classification and PCI. The resulting fee schedule is based on 2022 material and construction cost estimates; consequently, implementation including an annual adjustment for inflation is recommended. Since the negative impact of utility cuts is amplified when there are multiple or large cuts on the same street, and treatments are typically carried out across entire management sections, a full-recovery fee at a threshold of 10% of the management section area is appropriate. Example fee calculations were provided.

Empirical Green’s function analysis of some induced earthquake pairs from the Groningen gas field

AbstractWe have applied the empirical Green’s function (EGF) method to 53 pairs of earthquakes, with magnitudes ranging from M = 0.4 to M = 3.4, induced by gas production from the Groningen field in the Netherlands. For a subset of the events processed, we find that the relative source time functions obtained by the EGF deconvolution show clear indications of a horizontal component of rupture propagation. The earthquake monitoring network used has dense azimuthal coverage for nearly all events such that wavelet duration times can be picked as a function of source-station azimuth and inverted using the usual Doppler broadening model to estimate rupture propagation strike, distance, and velocity. Average slip velocities have also been estimated and found to be in agreement with typical published values. We have used synthetic data, from both a simple convolutional model of the seismogram and more sophisticated finite difference rupture simulations, to validate our data processing workflow and develop kinematic models which can explain the observed characteristics of the field data. Using a measure based on the L1-norm to discriminate results of differing quality, we find that the highest quality results show very good alignment of the rupture propagation with directions of the detailed fault map, obtained from the full-field 3D seismic data. The dip direction rupture extents were estimated from the horizontal rupture propagation distances and catalogue magnitudes showing that, for all but the largest magnitude event (the M = 3.4 event of 8th January 2018), the dip-direction extent is sufficiently small to be contained wholly within the reservoir.

Tidal Effects on the Schumann Resonance Amplitudes Recorded by the Global Coherence Monitoring System

The main scientific result of this paper is the demonstration of the fact that tidal effects induced by the Moon affect the Schumann resonance amplitudes measured at magnetometers located at different geographical locations of the Global Coherence Monitoring System. Each magnetometer is paired with the closest monitoring station of the global tidal wave measurement network. This paper introduces the Schumann Resonance Complexity Index (SRCI), computed by using the calibrated H-rank algorithm on the local magnetic field data recorded by each magnetometer of the Global Coherence Monitoring System. Tidal wave data recorded at each monitoring station are also used to compute the Tidal Wave Complexity Index (TWCI). Eliminating diel cycles from the SRCI data yields significant statistical correlations between the SRCI and TWCI data, proving the hypothesis of tidal effects on the global network of magnetometers.

Long-term vegetation trends and driving factors of NDVI change on the slopes of Mount Kilimanjaro

ABSTRACT This study is the first to examine vegetation change on Kilimanjaro from 2000 to 2022, using the Normalised Difference Vegetation Index (NDVI) at a spatial resolution of 250 m via the Moderate Resolution Imaging Spectrometer (MODIS). There is a long-term increase in vegetation on an annual basis, but with decreases in the long/short rains at low/high elevations. Temperature significantly influences NDVI across all elevations and seasons. Precipitation effects are more variable and often delayed, especially in the forest zone. The study explores how El Nino Southern Oscillation (ENSO) events affect NDVI differently at contrasting elevations, indicating the need for further elevation-specific remote sensing research and field data.

Turbulent Particle Flow in Spent Nuclear Fuel Storage Systems

Deposition models are being developed with the commercial computational fluid dynamics software STAR-CCM+ to evaluate particulate deposition on spent nuclear fuel (SNF) canisters. The primary particulate of concern is chloride salts, which are dispersed in the atmosphere and then deposited onto the canisters. During dry storage, the primary degradation process is likely to be chloride-induced stress corrosion cracking (CISCC) at the heat-affected zones of the canister welds. It is known that stainless steel canisters are susceptible to CISCC; however, the rate of chloride deposition onto the canisters is poorly known, based on sparse field data from a small number of sites. This paper describes work looking at various approaches to modeling turbulence, such as Reynolds-averaged Navier Stokes (RANS) and large eddy simulation (LES), and its impact on particle flow and deposition within a ventilated SNF storage system. The deposition rate is determined for a vertical canister system and a horizontal canister system. LES has the potential to simulate turbulent flows more accurately versus RANS, but is much more computationally expensive. A k-omega version of the RANS turbulence model was used for this study. The computational efficient RANS steady-state model predicted a similar canister deposition result as the LES simulation for a vertical canister storage system. For a horizontal storage system, the RANS steady-state model predicted more particles depositing on the canister than the LES simulation, providing a conservative estimation for particle deposition. A wall correction factor was added to the RANS model to dampen the turbulence fluctuation normal to a surface that left undamped, leads to the RANS model overpredicting deposition along a surface for smaller particles. This work was done to further development of deposition models that could be used to plan and inform SNF canister aging management programs with predictive models for the timing and occurrence of canister CISCC. This work is part of a larger effort tasked with understanding the likelihood of canister degradation due to CISCC. These models are still under development, and testing is needed for validation. However, these models are being presented now to demonstrate to canister vendors, utilities, regulators, and stakeholders the value of this type of modeling.

Data driven machine learning prognostics of buckling failure modes in ballasted railway track

This study explores the development and application of a machine learning (ML) approach to predict buckling failure modes in ballasted railway tracks. With the growing demand for safer and more reliable railway systems, the ability to foresee and mitigate track failures is of paramount importance. Our study focuses on harnessing advanced ML algorithms to analyse and interpret complex data sets, aiming to identify potential buckling failures before they occur. The methodology employed involves collecting extensive data from previous advanced numerical studies. Faced with the inadequacy of field data collection on track buckling and the limited availability of data related to track conditions, our study has relied on simulation data for insight and analysis. This data is then processed and analysed using sophisticated ML models, trained to recognise patterns and anomalies indicative of potential buckling failures. A novel aspect of our approach is the integration of environmental factors, acknowledging their significant influence on the likelihood of both snap-through and progressive buckling in railway tracks. We compare the effectiveness of various ML algorithms in accurately predicting these failure modes, evaluating their performance in simulated and real-world scenarios. The findings demonstrate the models' proficiency in identifying early signs of both snap-through and progressive buckling, leading to timely interventions. This capability not only improves railway safety but also aids in efficient maintenance scheduling and asset management. Additionally, a case study in Thailand's railway system demonstrates the model's effectiveness in predicting buckling failures under tropical environmental conditions. This paper contributes a novel perspective to the field of railway infrastructure maintenance. By providing a reliable method for predicting specific buckling failure modes, it paves the way for enhanced operational safety and efficiency in railway networks, particularly in the face of dynamic environmental conditions.

Leakage detection applied to the Da-Hedong Reservoir dam (China) based on the flow-field fitting method

Detecting leakage in concrete gravity dams presents a formidable challenge. The Flow-Field Fitting (FFF) method is used to identify leaks in China’s Da-Hedong reservoir dam. We propose vertical- and horizontal-gradient approaches based on the transmitted signal electrical field. A plastic frame is developed to allow the probe to measure horizontal gradients. We determine the exact location of leaks at the reservoir’s bottom and upstream face, respectively, assuming that current leakages are located in hydraulic leakage zones. Numerical experiments and water pressure tests reveal that the conductive silt layer beneath the reservoir can lower the response of the potential values at the leakage inlet, and the effect of the silt layer should be considered and not ignored when interpreting measured data. In addition, with regards to the principle of superposition, the contribution associated with metal pipes in the dam body can be roughly determined by subtracting it from the original potential field data, thereby aiding in locating leaks around the metal pipes. Additionally, the FFF measurements from the reservoir’s bottom also confirm the position of existing faults in the substratum at the reservoir site. Lastly, the results of the FFF method are tested against supporting evidence including ground penetrating radar and water pressure tests. In conclusion, these independent data confirm the effectiveness of the FFF method for leakage localization in the concrete gravity dam.

A computational topology-based method for extracting fault surfaces

Fault surface extraction is a crucial step in seismic interpretation, which can help structural interpretation and structural modeling. A key focus of fault surface extraction research is to extract as entire fault surfaces as possible, rather than just fault segments, which is more challenging in some complex fault situations. To address this challenge, we propose a fault surface extraction method based on computational topology to extract as entire fault surfaces as possible from a fault attribute and effectively handle some complex fault situations, such as intersecting faults. From a given seed point on the target fault, we utilize the idea of regional growth to search for high-confidence points on the target fault, called fault control points, under the constraints of the fault attribute and the calculated fault orientations. Through these fault control points, we extract the fault boundary and process the fault attribute so that only the target fault is included. Furthermore, we employ an operation in computational topology, collapse, to extract the target fault from the processed fault attribute using the fault boundary as a constraint. By incorporating fault orientation information and using a relatively large search distance during control point search, our method enables the integration of segmented faults and facilitates the handling of complex fault situations such as intersecting faults. The collapse operation ensures that the extracted fault surfaces align with the fault attribute, correspond to the actual fault locations in seismic data, and enhances fault continuity. Additionally, we present an automatic method for picking seed points to realize the extraction of all faults in the research data. We test our method on several field data sets and the experimental results demonstrate its effectiveness. In some complex fault situations, such as intersecting faults, our method performs well and shows a significant improvement over the compared method.

Linking crown fire likelihood with post-fire spectral variability in Mediterranean fire-prone ecosystems

Background Fire behaviour assessments of past wildfire events have major implications for anticipating post-fire ecosystem responses and fuel treatments to mitigate extreme fire behaviour of subsequent wildfires. Aims This study evaluates for the first time the potential of remote sensing techniques to provide explicit estimates of fire type (surface fire, intermittent crown fire, and continuous crown fire) in Mediterranean ecosystems. Methods Random Forest classification was used to assess the capability of spectral indices and multiple endmember spectral mixture analysis (MESMA) image fractions (char, photosynthetic vegetation, non-photosynthetic vegetation) retrieved from Sentinel-2 data to predict fire type across four large wildfires Key results MESMA fraction images procured more accurate fire type estimates in broadleaf and conifer forests than spectral indices, without remarkable confusion among fire types. High crown fire likelihood in conifer and broadleaf forests was linked to a post-fire MESMA char fractional cover of about 0.8, providing a direct physical interpretation. Conclusions Intrinsic biophysical characteristics such as the fractional cover of char retrieved from sub-pixel techniques with physical basis are accurate to assess fire type given the direct physical interpretation. Implications MESMA may be leveraged by land managers to determine fire type across large areas, but further validation with field data is advised.

Chat GPT from Educational, Legal and Ethical Perspectives in Indonesia

Objective: This article explores the concept of Chat GPT (Generative Pre-training Transformer) is an artificial intelligence that uses a conversation format. GPT chat is used in   education. There are advantages to using Chat GPT, on the other hand, there are weaknesses in using Chat GPT. The problem in this Research is the consequences of using GPT Chat for academic circles from a legal and ethical perspective.   Method: This Research uses a normative type of Research. The data used is secondary data that does not focus on field data. The secondary data used are primary legal materials and secondary legal materials.   Results and Conclusions: This Research concludes that the consequences of using GPT Chat for academics are seen from a legal and ethical perspective. GPT Chat has consequences for academics who commit ethical violations because it goes against the good things that humans believe. The use of GPT chat has consequences for academics deemed to have violated the law (copyright) if the use of academics' actions meets the qualifying elements of copyright infringement.   Research Implications: This research provides a comprehensive understanding of the consequences of using Artificial Intelligence, especially Chat GPT in the field of education. This understanding will provide direction for determining policies related to ideal educational goals, namely educating sportsmanship, honesty and respect for the work of others.

Large‐strain consolidation of vacuum preloading combined with partially penetrating prefabricated vertical drains

AbstractA system of vacuum preloading combined with partially penetrating prefabricated vertical drains (PP‐PVDs) is an effective solution for promoting the consolidation of the dredged marine clay. However, a significant and traditionally challenging‐to‐predict amount of deformation or settlement occurs. Therefore, it is necessary to introduce a three‐dimensional large‐strain consolidation model to consider the length of the vertical drain to determine the consolidation time and degree of consolidation (DoC), and associated settlement. The predictions using the proposed analytical model provide fair agreements with the field data and those in the literature. Parametric studies reveal that to achieve a 90% DoC within 100 days in soft soil, the optimal penetration depth for PP‐PVDs would be 0.7 times the depth of the soil layer. With the increase of DoC, the ratio of excess pore water pressure to applied vacuum pressure (u/P) in the whole soil layer moves toward 1.0. With the increase of PVD penetration ratio (H1/H), more DoC is required to dissipate the excess pore water pressure in the top improved soil layer.

Submarine cable detection using an end-to-end neural network-based magnetic data inversion

Abstract To process magnetic anomaly data, appropriate parameters for field separation, denoising, and Euler deconvolution have to be manually selected. The traditional workflow is inefficient and cannot fulfill the rapid detection of submarine cables due to the complex processing and manual parameter tuning. This study presents an end-to-end deep learning approach for the identification and positioning of submarine cables based on magnetic anomalies. The proposed approach effectively establishes a direct mapping correlation between the magnetic field data and the position of the submarine cable. Synthetic tests suggest that our method has a better performance in terms of positioning accuracy than the conventional Euler method. Our results for the field data are comparable to those obtained using conventional techniques. Furthermore, the proposed method achieves an optimal solution by employing clustering technique and selecting the solution with the maximum confidence, which avoids spurious solutions associated with traditional methods. The proposed method can directly determine the position of the submarine cables using the raw magnetic field data. Contrary to the traditional processing workflow, field separation and denoising are not necessary in this novel approach, resulting in higher processing efficiency and a simpler processing process.

The House Is Burning: Assessment of Habitat Loss Due to Wildfires in Central Mexico

Fire suppression and climate change have increased the frequency and severity of wildfires, but the responses of many organisms to wildfire are still largely unknown. In this study, we assessed the risk of habitat loss for amphibians, mammals, and reptiles caused by wildfires in central Mexico. We accomplished this by: (1) determining the likelihood of wildfire occurrence over a 12-year period using historical records and the Poisson probability mass function to pinpoint the most susceptible areas to wildfire; (2) evaluating species exposure by identifying natural land use that aligns with the potential distribution areas of biodiversity; (3) assessing species vulnerability based on the classifications established by the IUCN and CONABIO. Our findings have unveiled three regions exhibiting a concentration of high-risk values. Among these, two are positioned near major urban centers, while the third lies in the southeastern sector of the Nevado de Toluca protection area. Amphibians emerged as the taxonomic group most severely impacted, with a substantial number of species falling within the Critically Endangered and Endangered categories, closely followed by mammals and reptiles. Furthermore, we have identified a correlation between the location of risk zones and agricultural areas. This study revealed hotspots that can offer valuable guidance for strategic initiatives in fire-prone regions associated to the potential distribution of amphibians, mammals, and reptiles. Moreover, future studies should contemplate integrating field data to enhance our comprehension of the actual effects of wildfires on the spatial distribution of these animal groups.

ERT-based experimental integrated approach for soil hydrological characterization in rainfall-induced shallow landslides prone areas

Shallow landslides triggered by heavy rainfalls are slope instabilities, developed in the most superficial eluvial layers, involving the first 2 m from the ground level. A crucial predisposing factor in shallow landslides occurrence is the soil water content, generally measured trough sensors installed in the first soil layers. However, despite being a very precise approach, this monitoring technique provides for a site-specific dataset. An integrated method to extend the hydrological characterization from site-specific to slope scale is presented, combining geotechnical analyses, field data monitoring, and geophysical investigations, in two experimental test sites located on Italian Apennines. Ten Electrical Resistivity Tomographies (ERT) of the first soil horizons were performed through different array geometries (2D-3D-Time-Lapse), calibrated and interpreted basing on stratigraphic logs, trenches, and monitored soil water content field data. The test sites colluvial covers composition was analyzed and compared to resistivity values to build conceptual hydrogeological models of the deep-water circulation. In addition, two time-lapse (4D) ERT surveys were performed in both test sites simulating very intense precipitations, to determine the resistivity variations at different soil drainage conditions, thus estimating the average bulk permeability. Bulk permeability can be also a useful input parameter for slope stability models, widely employed in engineering practices. This integrated method proved to be very useful for the hydrogeological characterization of the subsoil at slope scale, where it is susceptible to slope instability, improving the knowledge of water circulation, as well as the bulk permeability heterogeneities, which are shallow landslides triggering parameters.

There and Back Again: A Perspective on 20 Years of CYP4Z1.

CYP4Z1, a highly expressed CYP gene in breast cancer, was one of the last CYPs to be identified in the human genome, some twenty years ago. CYP4 enzymes typically catalyze w-hydroxylation and metabolize w3 and w6 polyunsaturated fatty acids (PUFAs) to bioactive lipid metabolites that can influence tumor growth and metastasis. These attributes of CYP4Z1 make it an attractive target for new chemotherapeutic drug design, as a potential biomarker for selection of patients that might respond favorably to drugs and for developing enzyme inhibitors as potential therapeutic agents. This review summarizes the current state of knowledge regarding the advancing biochemistry of CYP4Z1, its role in breast cancer and the recent synthesis of selective chemical inhibitors of the enzyme. We identify gaps that need to be filled to further advance this field and present new experimental data on recombinant CYP4Z1 expression and purification of the active catalytic form. Significance Statement In breast cancer, an unmet need is the availability of highly effective therapeutic agents, especially for triple negative breast cancer. The relevance of the work summarized in this mini-review is that it identifies a new potential drug target, CYP4Z1, and discusses ways in which the gene product's catalytic activity might be modulated in order to combat this malignancy and limit its spread.

From seasonal field study to surrogate modeling: Investigating the biomechanical dynamics of Elymus sp. in salt marshes

AbstractSalt marshes have been studied in the context of ecosystem services they can provide for coastal protection. In this study, monthly field campaigns focusing on Elymus spp. and its biomechanical properties were conducted from December 2021 to December 2022 on the German Barrier Island Spiekeroog. A total of 1390 specimens were investigated to determine their growth length, out of which 418 specimens were investigated mechanically with three‐point bending tests to determine their biomechanical properties. To evaluate the interaction of hydraulic loads and vegetation, the challenge of modeling biomechanical plant properties to scale is addressed by using resin 3D printing with flexible material, while focusing on the materials mechanical properties. Based on the field data acquired and additional literature (adding up to 1959 measurements), a cylindrical plant model with an outer diameter of (scale 1 : 1) was developed. It was manufactured mixing two resin components with varying volume ratios resulting in surrogates with different flexural stiffnesses. The surrogates were characterized using three‐point bending tests and image analysis of their bending behavior when subjected to currents between 0.4 and 1.2 m/s. With the average Young's modulus ranging from 8.45 to 1708.42 MPa, the bending angle varies from 0° to 77.4° displaying the influence of material stiffness and flow velocity. Applying the Cauchy scaling law, this study shows that resin 3D printing can be used to model Elymus sp. with respect to its biomechanical properties allowing for seasonally independent physical laboratory experiments with plant models.

A national accounting framework for fire and carbon dynamics in Australian savannas

Background Tropical savannas represent a large proportion of the area burnt each year globally, with growing evidence that management to curtail fire frequency and intensity in some of these regions can contribute to mitigation of climate change. Approximately 25% of Australia’s fire-prone tropical savanna region is currently managed for carbon projects, contributing significantly to Australia’s National Greenhouse Gas Inventory. Aims To improve the accuracy of Australia’s national carbon accounting model (FullCAM) for reporting of fire emissions and sequestration of carbon in savanna ecosystems. Methods Field data from Australian savannas were collated and used to calibrate FullCAM parameters for the prediction of living biomass, standing dead biomass and debris within seven broad vegetation types. Key results Revised parameter sets and improved predictions of carbon stocks and fluxes across Australia’s savanna ecosystems in response to wildfire and planned fire were obtained. Conclusions The FullCAM model was successfully calibrated to include fire impacts and post-fire recovery in savanna ecosystems. Implications This study has expanded the capability of FullCAM to simulate both reduced emissions and increased sequestration of carbon in response to management of fire in tropical savanna regions of Australia, with implications for carbon accounting at national and project scales.

Numerical study of the impact of stress concentration on shale gas production

Predicting the production amounts has great significance for exploitation of oil and gas resources. The flow–geomechanical coupling effect plays an important role in predicting production. Shale rocks and the stress-dependent permeability model are critical for representing this coupling effect. The pore network of shale rock is not abundant like that of coal rock, and the matrix blocks of shale rock are not completely separated by the pore network. There is stress concentration around the pores when shale rock is deformed. Based on previous studies, the stress-dependent permeability model considering the impact of stress concentration is used in this study to build a numerical simulation model for flow–geomechanical coupling in a shale gas reservoir and validation using field data. Sensitivity studies are conducted to discuss the difference in production prediction between the new and common models. The main conclusions of this study are as follows: 1) The new model can fit both the field and experimental data well; the average error in daily production rate between the numerical solution and field data is 8%, which indicates that the new model can be used to predict shale gas production. 2) Under the geomechanical condition of a real reservoir, the difference in predicted production between models with and without stress concentration can be large and increase with the ratio of a to b. If this ratio is less than 50, the impact of stress concentration is not significant. Otherwise, the impact of stress concentration on production increases sharply and can exceed 20%. 3) The adsorption-induced strain enhances the impact of stress concentration on production. When the Langmuir pressure exceeds 1 MPa and Langmuir strain exceeds 0.01, the impact magnitude of stress concentration can exceed 10%.