Field Validation Research Articles

Interpreting Step-Drawdown Pumping Test Undergoing Confined-Unconfined Conversion with Well Loss.

The step-drawdown pumping test often experiences a transition from confined to unconfined conditions due to the continuously increasing pumping rate. However, the current well hydraulics model has not accurately interpreted this phenomenon. In this study, we developed an analytical solution to address the confined-unconfined conversion in step-drawdown pumping tests based on Girinskii's potential and superposition theory. Additionally, a field step-drawdown pumping test featuring confined-unconfined conversion was conducted to apply the proposed analytical solution. The particle swarm optimization algorithm was employed to simultaneously estimate multiple parameters. The results demonstrate that the newly proposed solution provides a better fit to the observed drawdown in the pumping well compared to previous models. The hydrogeological parameters (K, S), well loss coefficient (B), and critical time for confined-unconfined conversion (tc) were estimated to be K = 7.15 m/d, S = 6.65 × 10-5, B = 7.48 × 10-6, and tc = 1152 min, respectively. Neglecting the confined-unconfined conversion in step-drawdown pumping tests leads to underestimation of drawdown inside the pumping well due to an overestimation of the aquifer thickness. After the conversion from confined to unconfined conditions, the estimated well loss coefficient decreased by 88% compared to its pre-conversion value. This highlights the necessity of adjusting the well loss coefficient in the step-drawdown pumping test model to account for confined-unconfined conversion. In summary, this study introduces a new method for interpreting parameters in step-drawdown pumping tests and provides field validation for its effectiveness.

Field Validation of the Superelevation Method for Debris‐Flow Velocity Estimation Using High‐Resolution Lidar and UAV Data

AbstractEstimating flow velocities is key to assessing hazards associated with debris flows. One approach to post‐event velocity estimation is the superelevation method, which uses debris‐flow mudlines to measure the cross‐channel surface inclination, or superelevation, produced by centripetal forces acting on the flow in a bend. Flow velocities are then calculated using a subjective parameterization of the forced vortex equation modified to include a debris‐flow specific correction factor. Subjective parameterization of this equation leads to substantial variability and uncertainty in the resulting flow velocities. We present an analysis of the reliability of the superelevation method using a large UAV‐based data set of 14 debris flows with front velocities of ∼0.8–6.5 m s−1 and cross‐channel surface inclinations of ∼0.6–8.5°, as well as a validation for a single debris flow measured using high‐resolution, high‐frequency 3D lidar data fused to video imagery. The validation event indicates that when the flow surface inclination can be measured directly, the forced vortex equation produces excellent results without needing a correction factor for Froude numbers ranging from 0.7 to 1.5. This finding indicates that the main challenge with the superelevation method lies in obtaining accurate measurements of superelevation from the mudlines, and that a correction factor may serve to compensate for measurement difficulties rather than variable flow properties. For very small and highly subcritical flows, the superelevation method may generate a large overestimation of flow velocities.

Development and field validation of pumpable alkali-activated ground granulated blast furnace slag binder-based concrete using sodium gluconate as a retarder

Development and field validation of pumpable alkali-activated ground granulated blast furnace slag binder-based concrete using sodium gluconate as a retarder

Calcium-Binding Protein and Polymorphism in Musa spp. Somaclones Resistant to Fusarium oxysporum

The fresh fruits of ‘Grande Naine’ (Cavendish AAA—Musa spp.) dominate the world market, especially in countries with a population in a situation of social vulnerability. However, Fusarium wilt, caused by the fungus Fusarium oxysporum f.sp. cubense race 4 Subtropical (Foc ST4), emerges as a serious threat to banana production, requiring the development of resistant cultivars based on biotechnological strategies, such as the induction of mutation in tissue culture. This study aimed to identify and characterize genetic variation in somaclones resistant to Fusarium oxysporum f.sp. cubense subtropical race 4 (Foc ST4), derived from ‘Grand Naine’ bananas, by molecular markers based on retrotransposons IRAP (Inter-retrotransposon Amplified Polymorphism) and REMAP (Retrotransposon-Microsatellite Amplified Polymorphism). Nine combinations of IRAP and six combinations of REMAP primers were used. The low number of polymorphic bands did not allow for genetic diversity studies; however, ten polymorphic bands between the somaclones and control were sequenced. Of these, three presented good base calling and were aligned, namely, 1AF, 2AF, and 3AF bands. Only the 1AF band presented function related to stress response with homology to a calcium-binding protein. These proteins act early in plant infection as secondary messengers activated by pathogen-associated molecular patterns (PAMPs), initiating the cascade of plant defense signals. The fact that this band is present in all somaclones reinforces previous assessments of their resistance to Foc ST4. The use of markers IRAP and REMAP produced polymorphic bands that can, through future primer design and field validations, accelerate the identification of resistant banana genotypes for use in banana genetic breeding programs.

Evaluation and maintenance method for general speed railway turnouts based on multi-source data

In recent years, with the increasing passengers' requirements for safety and comfort, as well as the measures of increasing revenue and reducing the expenditure of railway company, more efficient, more scientific and more comprehensive requirements have been put forward for the maintenance of railway turnouts. This paper develops a maintenance strategy based on the Turnout Service State Indicator (TSSI), derived from an analysis of multi-source data concerning turnout conditions. The weights of critical indicators affecting turnout conditions are determined using Analytical Target Cascading (ATC) and Analytic Hierarchy Process (AHP). Subsequently, turnouts are classified into four categories—great, good, general, and poor—based on their TSSI values, guiding specific maintenance strategies. The proposed assessment method was validated using data from four sets of turnouts collected on actual tracks, showing consistency between the assessment results and field technicians' evaluations. A comparative analysis of the ATC and AHP suggests that the latter provides a more scientific and objective assessment of turnout conditions. The established methods for evaluating the service state and deciding on turnout maintenance actions have been proven feasible and practical through field validation. These methods can be broadly applied to maintain general speed railway turnouts to enhance repair efficiency and reduce maintenance costs.

PcMNet: An efficient lightweight apple detection algorithm in natural orchards

Apple detection plays a critical role in enabling the functionality of harvesting robots within natural orchard environments. To address challenges related to low detection accuracy, slow inference speed, and high parameter count, we present PcMNet, a lightweight detection model based on an improved YOLOv8 network. Initially, we employed Partial Convolution (Pconv) to construct a PR module, forming the Pconv-block, which subsequently replaced the original C2f feature extraction module within the YOLOv8n backbone. This replacement led to improvements in both detection accuracy and speed, while simultaneously reducing computational complexity (FLOPs), parameter count, and model size. Furthermore, the Cross-Scale Feature Fusion (CCFF) module was refined into Faster-Cross-Scale Feature Fusion (Faster-CCFF) with the integration of Pconv-block, significantly enhancing the model's feature extraction and fusion capabilities. Additionally, we introduced Mixed Local Channel Attention (MLCA) to further strengthen the model's capacity to capture essential features while effectively suppressing background noise. Experimental results demonstrate that PcMNet achieved a detection accuracy of 92.8 % and an mAP@0.5 of 95.5 %, representing improvements of 1.4 and 0.7 percentage points, respectively, over YOLOv8n. Moreover, PcMNet successfully reduced FLOPs, parameter count, and model size to 5.1 G, 1.4 M, and 3.2 MB, respectively. The per-image detection time was reduced to 2.3 ms, indicating reductions of 37.80 %, 53.33 %, 49.21 %, and 56.60 % in FLOPs, parameters, model size, and detection time compared to YOLOv8n. When deployed on edge computing devices with TensorRT acceleration, PcMNet achieved a detection rate of 92 FPS. Field validation experiments conducted in natural orchard environments confirmed PcMNet's superior ability to detect apples under challenging conditions, such as occlusions and varying lighting conditions. Its lightweight design and rapid detection capabilities provide a valuable reference for achieving real-time apple detection in automated and intelligent harvesting robots, thereby contributing to advancements in smart agriculture.

Research on Monitoring the Speed of Glacier Terminus Movement Based on the Time-Series Interferometry of a Ground-Based Radar System

The Tibetan Plateau (TP) is the largest glacier reserve outside the Antarctic and Arctic regions. Climate warming has affected the reserve of freshwater resources and led to frequent glacier disasters. However, due to its extreme environment of hypoxia and low pressure, it is extremely difficult to obtain data. Compared with other traditional monitoring methods such as makers and satellite remote sensing technology, Ground-Based (GB) radar systems have the advantages of convenient carrying and installation, sub-second level sampling, and sub-millimeter measurement accuracy. They can be used as an effective way to study the short-term rapid movement changes in glaciers. Based on a self-built GB radar system, monitoring experiments were conducted on two glacier termini on the TP. The movement speed of the Rongbuk glacier terminus on Mount Qomolangma was obtained through time-series interferometric measurement as 4.10 cm/day. When the altitude was about 5200 m, the glacier movement speed was 7.74 cm/day, indicating the spatial differences with altitude changes. And in another region, the movement speed of the Yangbulake glacier terminus on Mount Muztag Ata was 198.96 cm/day, indicating significant changes in glacier movement. The cross-validation of Sentinel-1 data during the same period proved the effectiveness of GB radar system interferometry in measuring glacier movement speed and also provided field validation data for remote sensing inversion.

A graphics-based digital twin (GBDT) framework for accurate UAV localization in GPS-denied environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) is crucial for effective assessment of large-scale civil infrastructure, as manual UAV control is time consuming and prone to mishaps. Autonomous navigation outdoors typically employs GPS signals to enable accurate localization and reduce long-term drifts. However, in many civil engineering applications, GPS signals are either poor or unavailable due to interference and/or multi-path effects. Current approaches for UAV localization in GPS-denied environments, track geometric features such as corners; however, these natural features can be misrepresented due to the presence of occlusions and/or image processing errors, resulting in significant localization errors that can grow with time. AprilTags can reduce localization errors, but installation on large-scale civil infrastructure can be challenging. Therefore, this paper proposes a framework that leverages the wealth of visual and geometric information encoded in a Graphics-Based Digital Twin (GBDT) of a target infrastructure to provide accurate localization of a UAV. The GBDT is comprised of a computer graphics model that faithfully represents a target structure’s geometry, structural features, and visual textures. The visual details of the GBDT are exploited to design an object recognition algorithm that detects GBDTtags, which are distinctive objects (or a collection of components) on the target structure in advance; GBDTtags provide functionality similar to AprilTags. When the camera attached to a UAV detects one or more GBDTtags, the vertices of the GBDTtags are mapped from the image plane into the GBDT coordinate system, allowing for the UAV to be localized. The framework, termed herein as GBDTpose, is first validated numerically using Blender, Gazebo, and Mavros software-in-the-loop (SITL). Subsequently, field validation is carried out using the Kavita and Lalit Bahl Smart Bridge at the University of Illinois Urbana-Champaign (UIUC). Results show that localization in GPS-denied environments can be achieved with 5-50 cm accuracy without the need for physical markers being placed on the structure.

Fluoro-Electrochemistry Based Phytoplankton Bloom Detection and Enumeration; Field Validation of a New Sensor for Ocean Monitoring

Fluoro-Electrochemistry Based Phytoplankton Bloom Detection and Enumeration; Field Validation of a New Sensor for Ocean Monitoring

Enhanced lithological mapping in arid crystalline regions using explainable AI and multi-spectral remote sensing data

Lithological classification is essential for understanding the spatial distribution of rocks, especially in arid crystalline areas. Artificial intelligence (AI) recent advancements with multi-spectral satellite imagery have been utilized to enhance lithological mapping in these areas. Here we employed different AI models namely, Support Vector Machine (SVM), Random Forest Classification (RFC), Logistic Regression, XGBoost, and K-nearest neighbors (KNN) for lithological mapping. This was followed by the application of explainable AI (XAI) for lithological discrimination (LD) which is still not widely explored. Based on the highest accuracy and F1 score of the previously mentioned models, RFC model outperformed all of them, and hence, it was integrated with XAI, using the SHapley Additive exPlanations (SHAP) method.This approach successfully identified critical multi-spectral features for LD in arid crystalline zones when applied on the Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and SRTM-DEM datasets covering the Hammash and the Wadi Fatimah areas in Egypt and the Kingdom of Saudi Arabia, respectively. Field validation in the Hammash area confirmed the RFC model's efficacy, achieving a satisfactory 94% overall accuracy for 18 features. SHAP was able to identify the top ten features for proper LD over the Hammash area with 90.3% accuracy despite the complex nature of the ophiolitic mélange. For validation purposes, RCF was then utilized in the Wadi Fatimah region, using only the top 10 critical features rendered from the SHAP analysis. It performed well and had 93% accuracy. Notably, XAI/SHAP results indicated that elevation data, Landsat-8's Green Band (B3), and the two ASTER SWIR bands (B5 and B6) were essential and significant for identifying island arc rocks. Moreover, the SHAP model effectively delineated complex mélange matrices, primarily using ASTER SWIR band (B8). Our findings highlight the successful combination of RFC with XAI for LD and its potential utilization in similar arid crystalline environments worldwide.

인구교육을 위한 메모로 프로젝트 수업 설계 모형 개발

Objectives The purpose of this study is to develop and validate a concrete educational method for effectively implementing population education in schools by designing the Memoro Project instructional design Model. Methods The instructional design model was developed based on the components of the Memoro Project. The initial model was revised through expert review. The revised model was further refined through field validation, leading to the development of the final Memoro Project Instructional Design Model. Results Instructional design model of the Memoro project consists of the stages of pre-analysis, design, preparation, execution, and evaluation. The stages in the design are comprised of memory collection planning, memory collection practice, memory collecting, and memory sharing. This instructional design model were found to enhance intergenerational understanding among students and aid in comprehending the lesson content. Additionally, it provided a positive experience for the elderly. Conclusions Instructional design model of the Memoro project is significant in that it proposes a concrete method for implementing population education in schools. Moreover, it is an effective population education method that can have a positive impact not only on schools but also on society as a whole.

Calibration of TEROS 10 and TEROS 12 electromagnetic soil moisture sensors

AbstractThe TEROS 10 and TEROS 12 are widely used capacitance‐based sensors for measuring volumetric water content (), but few systematic studies of the measurement volume and accuracy of these sensors have been published. The objectives of this study were to (1) calibrate and validate these sensors in mineral soils, (2) determine the sensing volume of each sensor, and (3) evaluate the temperature sensitivity of the sensors. Both sensors were calibrated in the laboratory using columns of packed soil at multiple moisture levels from air‐dry to near saturation. Sensors were validated using additional laboratory and field measurements. The sensing volume was determined by quantifying the response of raw sensor outputs while increasing the level of oven‐dry sand, moist sand (0.100 cm3 cm−3), or water around the sensor in the radial and axial directions. Temperature sensitivity was tested in controlled conditions over a range of temperatures from 2°C to 40°C using encapsulated sensors in packed soil columns at constant . Linear calibration models resulted in mean absolute error ≤0.030 cm3 cm−3 for both sensors during laboratory and field validations. In moist sand, the sensing volume contributing 95% of the maximum sensor response was 439 cm3 for the TEROS 10 and 423 cm3 for the TEROS 12. Both sensors exhibited changes in of ≤0.02 cm3 cm−3 when subjected to a temperature change from 2°C to 40°C. For the soils considered here, both sensors demonstrated accuracy that is likely sufficient for a variety of agricultural and hydrological applications.

Solar Field and Receiver Model Validation of the Next-CSP MW-Scale Prototype

This study presents a comparison of both modelling and experimental results obtained on the solar field and the receiver of the MW-scale particle driven CSP unit implemented at the Themis solar tower (France) in the framework of the Next-CSP H2020 European project. At partial load, ~900 kW, the simulated data concerning the incident power at the receiver aperture are consistent with the measured values with less than 5% difference from the experimental results. The difference is higher for the particle temperature and the thermal efficiency as a function of particle mass flow rate. It ranges between 12 and 98°C for measured particle temperature of 430 and 300°C respectively. For the thermal efficiency, the difference varies strongly with the experiments from approximately 12% to 50% (relative). The main cause of discrepancy between the experimental and the calculated results is attributed to the heterogeneity of the solar flux distribution on the receiver tubes.

A Bibliometric Analysis and Review on Applications of Industrial By-Products in Asphalt Mixtures for Sustainable Road Construction

The growing consumption of natural resources to meet the needs of road construction has become a significant challenge to environmental sustainability. Additionally, the increase in industrial by-products has raised global concerns due to their environmental impacts. The utilization of industrial by-products in asphalt mixtures offers an effective solution for promoting sustainable practices. The objective of this article is to conduct a bibliometric analysis and citation-based review to characterize and analyze the scientific literature on the use of steel slag aggregates, copper slag, phosphorus slag, bottom ash, fly ash, red mud, silica fume, and foundry sand in asphalt mixtures. Another aim is to identify research gaps and propose recommendations for future studies. The bibliometric analysis was conducted using VOSviewer software version 1.6.18, focusing on authors, co-authorship, bibliographic coupling, and countries. A total of 909 articles were selected for the bibliometric analysis. The findings indicate that more effort is needed to expand the application of industrial by-products in asphalt mixtures. Furthermore, these by-products should be utilized in different types of asphalt mixtures. The incorporation of industrial by-products into asphalt mixes also requires field validation and further laboratory investigations, particularly concerning aging and moisture resistance. In addition, the effects of chemical reactions involving industrial by-products on the long-term performance of asphalt layers should be evaluated. Finally, this article encourages engineers and researchers to intensify their efforts in utilizing industrial by-products for environmental sustainability.

Addressing the Difficulties and Opportunities to Bridge the Integration Gaps of Bio-Based Insulation Materials in the European Construction Sector: A Systematic Literature Review

Bio-based insulation materials (BbIMs) represent a potential alternative to conventional insulations, with their characteristics that favor a negative-carbon built environment. However, their use may face challenges that could prevent them from being used on a large scale in certain countries. The current study aims to provide focused insights into the practical difficulties and market opportunities for the application of BbIMs in Europe through a systematic literature review (SLR). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used as the basis for the conduct and reporting of this review. A keyword search was performed in Web of Science, Scopus, and ScienceDirect databases to select peer-reviewed English-language articles. HubMeta web tool was used to organize the selection process. The quantitative visualization of the literature was made by the Bibliometrix R package V4.1.4. Data were manually extracted and clustered in an Excel sheet. The review included 28 studies that have revealed interrelated insights. Difficulties range from regulatory and policy limitations and variability in performance, such as microbial growth and inconsistency in the behavior of materials under different conditions, to cost barriers. However, there are promising opportunities, including policy incentives and material performance benefits such as improved energy efficiency and indoor air quality. This research contributes to the literature by providing focused insights into the practical difficulties and market opportunities for the application of BbIMs in Europe. Research gaps and future perspectives point to the need for more field validation experiments, exploration of alternative production processes, and expanding life cycle assessment scopes to optimize their integration and performance. Stakeholders’ perceptions were made with a small sample in certain countries. Stakeholder perceptions were conducted with a small sample in some countries, so insights from stakeholders are needed to confirm or correct current findings.

Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Realizing accurate determination of in situ soil organic matter (SOM) content and soil moisture (SM) content in the field is of great importance for improving agricultural production efficiency. However, the feature information of a single sensor is limited, and there is still a certain gap in modeling accuracy compared to traditional laboratory methods. The variations of SM in the field also interfere with data collection, limiting the application of single sensor. In order to realize the efficient detection of in-situ SOM and SM content, this study developed an online detection system for SOM and SM content based on the fusion of characteristic wavelengths, visible images and thermal imaging image features. Firstly, based on a vehicle-mounted platform, a visible-thermal imaging camera and a characteristic wavelength integration device were integrated in a deep pine plow to realize the simultaneous acquisition of in-situ soil multi-sensor data. Then, a lightweight multimodal network was constructed to obtain thermal visible light image features and characteristic wavelength features through branch networks, achieving deep fusion of different modal data and predicting SOM and SM content. Finally, output the forecasting results of SOM and SM content, and transmit the predictive information to the cloud platform for storage. It was verified that the Multi-modal proposed in this study worked best in the laboratory environment, with a predicted R2 of 0.91 and RMSE of 2.9 g/kg for SOM, and a predicted R2 of 0.92 and RMSE of 0.77 % for SM. Compared with single image or characteristic spectral data, the fusion of visible image and characteristic wavelength effectively improved the prediction accuracy of SOM. The real-time prediction of SM was realized by fusing thermal imaging data, and the elimination of the moisture effect of visible images and characteristic wavelengths was also realized with the help of deep fusion of Multi-modal network. After field validation, the R2 of the Multi-modal system was 0.84 and the RMSE was 5.0 g/kg for SOM, and the R2 of the SM was 0.88 and the RMSE was 1.03 %. Despite the differences in soil types between the validation field and the sampling field, the system still demonstrated strong generalization and achieved high accuracy prediction of in-situ SOM and SM in the field, which effectively improves the efficiency and applicability of field. The efficiency and applicability of soil testing effectively improve the efficiency and provide technical guidance for precision management in the field.

Robust online monitoring system for PCDD/Fs in a full-scale MSWI by Deans switch: Efficiently separation and purification

Existing online monitoring system for polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), technically named Thermal Desorption-Gas Chromatography-Tunable Laser Ionization-Time of Flight Mass Spectrometry (TD-GC-TLI-TOFMS), has been applied in several incinerators in China. TD-GC-TLI-TOFMS can realize rapid detection of PCDD/Fs emissions from incineration sources. However, the long-term measurement of unclean flue gas will pollute the instruments in TD-GC-TLI-TOFMS, and interfere with the peak output of the target 1,2,4-trichlorobenzene (1,2,4-TrCBz). In this study, Deans switch (DS) was utilized for the first time in an online monitoring system for PCDD/Fs to separate 1,2,4-TrCBz signal from impurity signals, which improved the anti-interference capability of the system. Laboratory standard gas experiments showed that after adding a DS device between GC and TLI pulse valve, when the pressure set in DS was 4 psi and switched before or near the peak output of 1,2,4-TrCBz, the change of 1,2,4-TrCBz signal intensity was minimal. The impurities near the target peak were removed, and TLI-TOFMS was highly stable during continuous measurement. Moreover, the maximum intensity peak time of 1,2,4-TrCBz was stable after using DS in different switching time intervals. When connecting DS to TD-GC-TLI-TOFMS for field validation on the tail flue gas of a municipal solid waste incinerator (MSWI), results showed that a better 1,2,4-TrCBz signal could be obtained with a 69.52 % reduction of impurity peaks at the moments closer to the target peak. Furthermore, DS improved the sensitivity of the system to low concentration variations of 1,2,4-TrCBz in the flue gas. The robust system developed in this study can be better applied to incineration factories with poor combustion or suboptimal purification technology, facilitating online PCDD/Fs monitoring.

Detection of the jellyfish Chrysaora pacifica by RPA-CRISPR-Cas12a environmental DNA (eDNA) assay and its evaluation through field validation and comparative eDNA analyses

Climate-driven environmental changes and anthropogenic activities can result in the proliferation of non-indigenous aquatic species such as jellyfish that may cause envenomation and various ecological disruptions. Here we developed a two-step RPA-CRISPR-Cas12a eDNA assay, consisting of target eDNA amplification followed by a CRISPR-Cas12 reaction, for the early detection of Chrysaora pacifica, a jellyfish species often considered non-indigenous to South Korea. The assay demonstrated high sensitivity, with a detection limit of two copies COI/μL for eDNA derived from C. pacifica, using target specific RPA primers and crRNA sequences. Field validation of the assay using eDNA samples from Jinhae Bay collected over eight months of time-series monitoring, revealed temporal distribution of the jellyfish which correlated with results of digital polymerase chain reaction (dPCR) and eDNA metabarcoding. The C. pacifica eDNA assays were also corroborated (R-square 0.7891) by reports from a citizen science–based jellyfish-monitoring program operated by the National Institute of Fisheries Science, South Korea. Our RPA-CRISPR-Cas eDNA assay can therefore, be an efficient alternative to traditional tools for the early detection of outbreaks of non-indigenous or harmful species in marine ecosystems.

Decision-making method of managed pressure drilling based on real-time calculation and analysis models

Abstract As petroleum exploration and development extend towards marginal, deep, and unconventional resources, drilling challenges such as narrow density windows and high wellbore control risks become increasingly prominent. Managed pressure drilling (MPD) proves effective in reducing drilling operational difficulties and well control risks in narrow safe-density window formations. However, existing MPD systems have limited capabilities for real-time calculation and analysis in the absence of downhole pressure measurement data, hindering accurate guidance for real-time calculation, analysis, and control decision recommendations in both conventional and precise MPD operations.Therefore, based on on-site engineering logging, real-time data from MPD operations, a branch decision tree was established to automatically determine the operational conditions of MPD. This achieved real-time identification of 11 drilling conditions. Starting from the characteristics of two-phase fluid flow in the annulus, coupling wellbore pressure and formation fluid invasion, a predictive model for annular pressure distribution during MPD was established. This model facilitated the calculation, determination, and recommendation of the pre-control value for wellhead backpressure. Building upon theoretical research, a MPD calculation, analysis, and control decision system was developed. Field testing and validation indicated that the system could simulate and analyze wellbore pressure and wellhead backpressure values, automatically identify drilling conditions and downhole complexities. The average error between the calculated and measured values of the standpipe pressure was 1.88%. The accuracy of the predicted wellhead backpressure values based on safe operating conditions compared to the actual controlled values reached 89.8%. This system achieved real-time calculation, analysis, and control decision-making based on real-time engineering parameters, enhancing the reliability and timeliness of wellhead backpressure control in MPD operations. It effectively guided conventional MPD operations and improved the capabilities and intelligence level of precision MPD operations after process optimization.

Landslides and flood hazard mapping using geomorphological methods in Santa Ana, Costa Rica

This study addresses the critical need for comprehensive hazard maps to guide urban and periurban risk management in Santa Ana, Costa Rica. Located near the capital, San Jose, Santa Ana is a rapidly developing municipality characterized by high-value properties, commercial zones, luxury housing, agricultural, and protected areas. However, it faces significant challenges from landslides and floods. Despite its vulnerability, the municipality lacks detailed, holistic hazard assessments. To bridge this gap, we employed geomorphological methods, including morphometry and morphogenetics, integrated with geographic information systems (GIS), historical data, and on-site validation. Our analysis identified that slopes exceeding 20° are particularly susceptible to landslides, with valleys and unstable hillslopes being critical zones for seismic and water-induced destabilization. For flood hazards, areas with low slope and high flow accumulation, such as alluvial fans and flood plains, were found to be highly susceptible. Field and mapping validation against the DesInventar disaster database confirmed the accuracy of our hazard zones. These findings provide essential geospatial insights for effective risk management, decision-making, and territorial planning, enabling proactive and adaptive responses to natural hazards in Santa Ana. While this study does not present radical methodological innovations, its main contribution lies in demonstrating how easy-access and practical geomorphological tools can be used to generate valuable information on risk zoning in regions with limited resources. Furthermore, the methodology and insights from this study are applicable to other urban and periurban regions facing similar geomorphic hazards, highlighting its broader relevance.