Test Data Research Articles

Quantifying the Role of Calibration Strategies on Surface‐Subsurface Hydrologic Model Performance

ABSTRACTDistributed, coupled surface‐groundwater hydrologic models are high‐dimensional, given the necessity to reflect the spatially diverse nature of complex hydrologic processes. Furthermore, inverse/inference problems involving these high‐dimensional models are naturally ill‐posed, given the limited information content of state observations that are typically assimilated. Many inversion/inference algorithms do not cope well with high dimensionality, leaving the practitioner to make subjective choices related to uncertain model inputs. The objective of this study is to evaluate the impact of these subjective calibration choices within a formal sensitivity analysis, uncertainty analysis, and parameters estimation (SA‐UA‐PE) framework on model testing for a surface‐subsurface hydrologic model. In doing so, we address the concepts of ‘over‐parameterisation’ and ‘under‐parameterisation’. We completed a series of numerical experiments, testing several otherwise subjective aspects of the calibration process: (1) the number (5, 10, 15, 20) and type (soil, aquifer, land surface, channel) of calibration parameters selected); (2) the type of state observations assimilated (streamflow, groundwater head); and (3) the length of testing period (1 to 14 years), using monthly streamflow and groundwater head as testing data. The experiments were completed for models of the Winnebago River watershed (Minnesota, Iowa), (significant tile drainage) and the Nanticoke River watershed (Delaware, Maryland (significant groundwater‐channel interactions). The selected hydrologic model is SWAT+, using the gwflow module for physically based groundwater storage and flow modelling, and simulations are run for the 2000–2015 period. Through this process, we found that increasing the number of parameters from 5 to 15 improves the representation of streamflow, principally through an improvement of groundwater storage representation and baseflow generation, but minimal improvement when increasing to 20 parameters. Therefore, the SA‐UA‐PE process can be optimised based on an ideal number of parameters that yield adequate results while maintaining a lower computational burden. The method presented here can be used for any watershed, using integrated surface‐subsurface hydrologic models.

Normative data for tests of visuo-spatial, visuo-constructional skills, and visual memory for Spanish-speaking adults in the United States

BACKGROUND: The Rey-Osterrieth Complex Figure Test (ROCFT) and the Clock Drawing Test (CDT) are commonly used in clinical practice. The ROCFT measures constructional praxis, visual perception, and visuospatial learning and memory, and the CDT assesses for visuospatial, constructional, and executive difficulties. Several neurological disorders are associated with visuospatial and visuo-constructional impairments, yet reliable normative data accounting for sociodemographic and acculturative variables are scarce for Hispanics living in the U.S. OBJECTIVE: To generate normative data for the ROCFT and CDT in a Spanish-speaking adult population living in the U.S. METHODS: The sample consisted of 245 cognitively healthy adults recruited from several states in the U.S. Each participant was administered the ROCFT and CDT as part of a larger cognitive battery. The ROCFT and CDT were normed using a Bayesian approach. Age, age2, education, sex, acculturation, and language proficiency were included as predictors in the analyses. RESULTS: ROCFT performance was associated with education and age, particularly as they interacted with Spanish language proficiency and time spent in the U.S. Education was significantly associated with recall abilities and a lower memory recall on the ROCFT. Age was found to vary depending on a person’s bilingual abilities. Sex did not emerge as a predictor of performance, and it did not interact significantly with other variables. CONCLUSION: This is the first study to include acculturation and language proficiency variables in the creation of norms for the assessment of visuo-constructional abilities. This study will have a large impact on the practice of neuropsychology in the U.S.

PENGEMBANGAN SISTEM IOT UNTUK PEMANTAUAN KESEHATAN DOMBA DENGAN ALGORITMA C4.5 BERBASIS THINGSPEAK

Effective livestock health monitoring is one of the primary challenges in modern farming, especially in detecting early health disorders in sheep. This research aims to develop a sheep health monitoring system based on the Internet of Things (IoT) using the C4.5 algorithm and the ThingSpeak platform. The system collects vital sheep data such as body temperature, heart rate, sound, and physical activity in real time through sensors and microphones connected to IoT devices. The data is then transmitted to the ThingSpeak platform for analysis and storage. The C4.5 algorithm is used to build a decision model capable of classifying the health conditions of sheep based on collected parameters such as temperature, heart rate, and respiration. The processed data results are displayed in the form of graphs and warning notifications on the ThingSpeak platform, allowing farmers to monitor livestock health easily and responsively. The accuracy test yielded a 90% accuracy rate using a confusion matrix with a data sampling split of 80% for training data and 20% for testing data. This indicates that the system has a high level of accuracy in detecting sheep health conditions. Consequently, the system has the potential to assist farmers in improving the efficiency of livestock health monitoring automatically and in real time. Moreover, the application of IoT technology and the C4.5 algorithm in the livestock sector is expected to provide innovative solutions to support productivity and animal welfare.

Methodology for the generation of normative data for the U.S. adult Spanish-speaking population: A Bayesian approach.

Hispanics are the largest growing ethnic minority group in the U.S. Despite significant progress in providing norms for this population, updated normative data are essential. To present the methodology for a study generating normative neuropsychological test data for Spanish-speaking adults living in the U.S. using Bayesian inference as a novel approach. The sample consisted of 253 healthy adults from eight U.S. regions, with individuals originating from a diverse array of Latin American countries. To participate, individuals must have met the following criteria: were between 18 and 80 years of age, had lived in the U.S. for at least 1 year, self-identified Spanish as their dominant language, had at least one year of formal education, were able to read and write in Spanish at the time of evaluation, scored≥23 on the Mini-Mental State Examination, <10 on the Patient Health Questionnaire- 9, and <10 on the Generalized Anxiety Disorder scale. Participants completed 12 neuropsychological tests. Reliability statistics and norms were calculated for all tests. This is the first normative study for Spanish-speaking adults in the U.S. that uses Bayesian linear or generalized linear regression models for generating norms in neuropsychology, implementing sociocultural measures as possible covariates.

The Influence of Structural Brain Changes on Cognition in the Context of Healthy Aging: Exploring Mediation Effects Through gBAT-The Graphical Brain Association Tool.

The contribution of age-related structural brain changes to the well-established link between aging and cognitive decline is not fully defined. While both age-related regional brain atrophy and cognitive decline have been extensively studied, the specific mediating role of age-related regional brain atrophy on cognitive functions is unclear. This study introduces an open-source software tool with a graphical user interface that streamlines advanced whole-brain mediation analyses, enabling researchers to systematically explore how the brain acts as a mediator in relationships between various behavioral and health outcomes. The tool is showcased by investigating regional brain volume as a mediator to determine the contribution of age-related brain volume loss toward cognition in healthy aging. We analyzed regional brain volumes and cognitive testing data (Montreal Cognitive Assessment [MoCA]) from a cohort of 131 neurologically healthy adult participants (mean age 50 ± 20.8 years, range 20-79, 73% females) drawn from the Aging Brain Cohort Study at the University of South Carolina. Using our open-source tool developed for evaluating brain-behavior associations across the brain and optimized for exploring mediation effects, we conducted a series of mediation analyses using participant age as the predictor variable, total MoCA and MoCA subtest scores as the outcome variables, and regional brain volume as potential mediators. Age-related atrophy within specific anatomical networks was found to mediate the relationship between age and cognition across multiple cognitive domains. Specifically, atrophy in bilateral frontal, parietal, and occipital areas, along with widespread subcortical regions mediated the effect of age on total MoCA scores. Various MoCA subscores were influenced by age through atrophy in distinct brain regions. These involved prefrontal regions, sensorimotor cortex, and parieto-occipital areas for executive function subscores, prefrontal and temporo-occipital regions, along with the caudate nucleus for attention and concentration subscores, frontal and parieto-occipital areas, alongside connecting subcortical areas such as the optic tract for visuospatial subscores and frontoparietal areas for language subscores. Brain-based mediation analysis offers a causal framework for evaluating the mediating role of brain structure on the relationship between age and cognition and provides a more nuanced understanding of cognitive aging than previously possible. By validating the applicability and effectiveness of this approach and making it openly available to the scientific community, we facilitate the exploration of causal mechanisms between variables mediated by the brain.

Trends in the Uptake of Index Testing among Adolescents in USAID-Supported PEPFAR Programs, October 2017- September 2022.

Adolescents 10-19 years account for a growing proportion of people living with HIV (PLHIV). In 2023, 140,000 adolescents were diagnosed with HIV, yet knowledge of HIV status and uptake of testing services remain critically low. Index testing - offering testing to contacts of PLHIV - is an important case-finding strategy. In 2021, PEPFAR expanded guidance to explicitly include older adolescents 15 to 19 years. We reviewed index testing data to assess uptake and case-finding trends among biological adolescent-aged children and siblings of PLHIV 10-19 years. Routinely collected programmatic data from 27 USAID-supported PEPFAR country and regional programs were analyzed for fiscal years (FY) 2017 through FY2022 (October 2016 - September 2022). We compared the volume of index testing and subsequent new diagnoses across FYs and countries among biological adolescent-aged children and siblings of PLHIV, and disaggregated by age, 10-14 and 15-19 years, and sex. Index testing among adolescents 10-19 years increased from FY17 to FY22, nearly doubling from 147,088 to 291,534. Similarly, new diagnoses among adolescents increased between FY17 and FY22 (3,721 vs 10,730). Overall, across FYs, index testing uptake and case-finding were higher among females than males, and the gap in testing uptake between sexes was larger for older than younger adolescents. Index testing uptake has increased substantially among adolescents over time, with rebounded gains for adolescents 15-19 years noted beginning in FY21. However, uptake across age and sex remained uneven, highlighting an opportunity to ensure targeted testing strategies are employed to reach adolescents 15-19 years and males.

Advanced Hybrid Techniques for Cyberattack Detection and Defense in IoT Networks

ABSTRACTThe Internet of Things (IoT) represents a vast network of devices connected to the Internet, making it easier for users to connect to modern technology. However, the complexity of these networks and the large volume of data pose significant challenges in protecting them from persistent cyberattacks, such as distributed denial‐of‐service (DDoS) attacks and spoofing. It has become necessary to use intrusion detection systems and protect these networks. Existing intrusion detection systems for IoT networks face many problems and limitations, including high false alarm rates and delayed detection. Also, the datasets used for training may be outdated or sparse, which reduces the model's accuracy, and mechanisms may not be used to defend the network when any intrusion is detected. To address these limitations, a new hybrid deep learning and machine learning methodology is proposed that contributes to detecting DDoS and spoofing attacks, reducing false alarms, and then implementing the necessary defensive measures. In proposed hybrid methodology consists of three stages: the first stage is to propose a hybrid method for feature selection consisting of techniques (correlation coefficient and sequential feature selector); the second stage is to propose a hybrid model by integrating deep learning neural networks with a machine learning classifier (cascaded long short‐term memory [LSTM] and Naive Bayes classifier); and in the third stage, improving network defense mechanisms and blocking ports after detecting threats and maintaining network integrity. In training and evaluating the performance of the proposed methodology, three datasets (CIC‐DDoS2019, CIC‐IoT2023, and CIC‐IoV2024) were used, and these data were also balanced to obtain effective results. The accuracy of 99.91%, 99.88%, and 99.77% was obtained. Also, a cross‐validation technique was used with the test data to ensure no overfitting. The proposed methodology has proven its high performance in detecting attacks, as it provides a powerful solution to enhance the security of IoT networks and protect them from cyberattacks, as it can be applied in many fields and to other attacks.

Analysis of Saturated Impulse for Square Plates under Flat Slamming Impact: Experimental and Numerical Investigation

The saturated impulse is a special phenomenon in the dynamic plastic behavior of engineering structures under intensive pulse loading, such as slamming loading. In this study, slamming experiments were performed on steel plates to investigate their slamming pressure and dynamic plastic responses, as well as the saturation phenomenon, and elucidate the effect of the plate thickness and material properties on the dimensionless saturated deflection and saturated impulse in combination with the published test data. The results show that the dimensionless saturated deflection and saturated impulse of the test plates gradually increased as the dimensionless stiffness decreased. After being validated against the experimental results, a numerical method that considered the fluid–structure interaction (FSI) effect was then employed to provide comprehensive insight into the transient plastic responses and saturated impulse of the flat plates under slamming impact. Numerical simulations revealed that the compressed air layer always existed during the effective process of the flat slamming impact. Through the numerical prediction of the dynamic plastic deflection and slamming pulse loading, it was observed that the saturated impulse phenomenon always took place after the time instant of the peak value of the pressure pulse. Furthermore, the analysis of the saturated impulse based on the numerical simulations indicated that the saturation phenomenon was more likely to be achieved as the water impact velocity increased.

Detection and Prediction of Toxic Aluminum Concentrations in High-Priority Salmon Rivers in Nova Scotia.

Elevated concentrations of toxic cationic aluminum (Ali) are symptomatic of terrestrial and freshwater acidification and are particularly toxic to salmonid fish species such as Atlantic salmon (Salmo salar). Speciated metal samples are rarely included in standard water monitoring protocols, and therefore the processes affecting Ali dynamics in freshwater remain poorly understood. Previous analysis of Ali concentrations in Nova Scotia (Canada) rivers found that the majority of study rivers had concentrations exceeding the threshold for aquatic health, but a wide-scale survey of Ali in Nova Scotia has not taken place since 2006 (Dennis, I. F., & Clair, T. A., 2012, Canadian Journal of Fisheries and Aquatic Sciences, 69(7), 1174-1183). The observed levels of dissolved aluminum in Atlantic salmon (Salmo salar) rivers of Atlantic Canada have potential serious and harmful effects for aquatic populations. We present the findings of the first large-scale assessment of the Ali status of Nova Scotia rivers in 17 years; we measured Ali concentrations and other water chemistry parameters at 150 sites throughout the Southern Uplands region of Nova Scotia from 2015 to 2022. We found that Ali concentrations exceeded toxic thresholds at least once during the study period at 80% of the study sites and that Ali concentrations increased during the study period at all four large-sample study sites. Modeling of relationships between Ali concentrations and other water chemistry parameters showed that the most important predictors of Ali are concentrations of the dissolved fractions of Al, iron, titanium, and calcium, as well as dissolved organic carbon and fluoride. We developed a fully Bayesian linear mixed model to predict Ali concentrations from a test data set within 15 μg/L. This model may be a valuable tool to predict Ali concentrations in rivers and to prioritize areas where Ali should be monitored. Environ Toxicol Chem 2024;00:1-12. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Measurement and prediction of vertical temperature distribution above urban building roofs at an ultra-microenvironment scale

Microenvironment is critical for building thermal performance. Heat transfer through building envelope (such as roof and window) and mass transfer through building mechanical systems that are adjacent to building surfaces (such as roof-top unit) are influenced directly by the air conditions near the surfaces (e.g., within 1.5 m). These air environments are within the sublayer of the air thermal boundary layer around the buildings – here defined as ultra-microenvironments, to be distinct from the conventional microenvironment scale of 2–5 m from building surfaces. This study particularly investigates the vertical temperature profiles within the ultra-microenvironments above urban building roofs. Field tests were conducted on two roof tops in Wuhan, China and Boulder, US, respectively. Consistent vertical air temperature profiles above the roofs were observed for both days and nights. The study further identifies one recent above-roof air temperature model and validates it with the field test data, which can effectively predict the vertical air temperature at various heights within the ultra-microenvironment above the roof. Applications of this model using different local and climate data including weather station data are analyzed and discussed, along with their performance on prediction accuracy.

An Efficient Method for Identifying Inter-Well Connectivity Using AP Clustering and Graphical Lasso: Validation with Tracer Test Results

Identifying inter-well connectivity is crucial for optimizing reservoir development and facilitating informed adjustments. While current engineering methods are effective, they are often prohibitively expensive due to the complex nature of reservoir conditions. In contrast, methods that utilize historical production data to identify inter-well connectivity offer faster and more cost-effective alternatives. However, when faced with incomplete dynamic data—such as long-term shut-ins and data gaps—these methods may yield substantial errors in correlation results. To address this issue, we have developed an unsupervised machine learning algorithm that integrates sparse inverse covariance estimation with affinity propagation clustering to map and analyze dynamic oil field data. This methodology enables the extraction of inter-well topological structures, facilitating the automatic clustering of producers and the quantitative identification of connectivity between injectors and producers. To mitigate errors associated with sparse production data, our approach employs sparse inverse covariance estimation for preprocessing the production performance data of the wells. This preprocessing step enhances the robustness and accuracy of subsequent clustering and connectivity analyses. The algorithm’s stability and reliability were rigorously evaluated using long-term tracer test results from a test block in an actual reservoir, covering a span of over a decade. The results of the algorithm were compared with those of the tracer test to evaluate its accuracy, precision rate, recall rate, and correlation. The clustering results indicate that wells with similar characteristics and production systems are automatically grouped into distinct clusters, reflecting the underlying geological understanding. The algorithm successfully divided the test block into four macro-regions, consistent with geological interpretations. Furthermore, the algorithm effectively identified the inter-well connectivity between injectors and producers, with connectivity magnitudes aligning closely with actual tracer test data. Overall, the algorithm achieved a precision rate of 79.17%, a recall rate of 90.48%, and an accuracy of 91.07%. This congruence validates the algorithm’s effectiveness in the quantitative analysis of inter-well connectivity and demonstrates significant potential for enhancing the accuracy and efficiency of inter-well connectivity identification.

Gradient Boosting: A Computationally Efficient Alternative to Markov Chain Monte Carlo Sampling for Fitting Large Bayesian Spatio-Temporal Binomial Regression Models

Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time. Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo (MCMC) methods are commonly used for such data. When the spatio-temporal support of the model is large, implementing an MCMC algorithm becomes a significant computational burden. This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatio-temporal mixed effects binomial regression model. We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach. Both methods are used to produce monthly forecasts for Lyme disease, anaplasmosis, ehrlichiosis, and heartworm disease in domestic dogs for the contiguous United States. The data have a spatial support of 3,108 counties and a temporal support of 108-138 months with 71 to 135 million test results. The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm, with a similar mean absolute prediction error.

Interpreting and Reporting Minimal Residual Disease Testing Data for Acute Lymphoblastic Leukemia Patients Using Next-Generation Sequencing

Interpreting and Reporting Minimal Residual Disease Testing Data for Acute Lymphoblastic Leukemia Patients Using Next-Generation Sequencing

Analysis and numerical simulation of different flowmeters based on an open channel in an irrigation area

ABSTRACT This study establishes an ultrasonic open channel flow measurement test and numerical simulation model and systematically compares the performance and reliability of the ultrasonic method and the traditional flowmeter in flow measurement in the irrigation area by observing and analysing the test data and combining it with the numerical simulation. In the indoor ultrasonic flowmeter flow measurement experiments in the trapezoidal nullah section, the flow rate measurement was carried out by using a high-precision variable slope flume and a variety of flow measurement tools. A numerical study was carried out using the VOF method to calculate the flow in the open channel flume under two-phase flow conditions. Combining experimental measurements and numerical simulations, this study aims to quantify the magnitude of flow measurement errors and water surface fluctuations in open channel flumes and to address the discrepancies between different flow measurement methods in open channels in irrigation areas.

Automobile Intelligent Vehicle-machine and Human-computer Interaction System based on Big Data

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable.

Data-Driven Method for Nonlinear Modal Interaction Identification in Fighter Aircraft

The purpose of this paper is to report a new data-driven analysis methodology for identifying nonlinear modal interactions and its application to aeroelastic wind-tunnel data from a generic fighter configuration, which reveals global modal interactions in this system caused by multiple resonance capture events initiated by local nonlinearities. It is shown that during supersonic testing at the Transonic Dynamics Tunnel, vibro-impacts at the embedded external fuel tank shakers on the inboard underwing stations of the generic fighter introduce strong nonlinearity that gives rise to nonlinear modal interactions. Specifically, time–frequency analysis of the acceleration data demonstrates that 6∶5 and 7∶6 transient resonance captures occur across the aircraft exclusively during this vibro-impact forcing window. Further analysis suggests that these resonance captures are dependent on Mach number. These results show experimentally that local vibro-impacts can cause global nonlinear effects in an aeroelastic system and that these effects can be identified using the new methodology herein. The data-driven analysis scheme employed shows applicability to full-aircraft flight test data for identifying nonlinear modal interactions without knowledge of the precise nonlinear contributors.

Generative adversarial networks to create synthetic motion capture datasets including subject and gait characteristics

Resource-intensive motion capture (mocap) systems challenge predictive deep learning applications, requiring large and diverse datasets. We tackled this by modifying generative adversarial networks (GANs) into conditional GANs (cGANs) that can generate diverse mocap data, including 15 marker trajectories, lower limb joint angles, and 3D ground reaction forces (GRFs), based on specified subject and gait characteristics. The cGAN comprised 1) an encoder compressing mocap data to a latent vector, 2) a decoder reconstructing the mocap data from the latent vector with specific conditions and 3) a discriminator distinguishing random vectors with conditions from encoded latent vectors with conditions. Single-conditional models were trained separately for age, sex, leg length, mass, and walking speed, while an additional model (Multi-cGAN) combined all conditions simultaneously to generate synthetic data. All models closely replicated the training dataset (<8.1 % of the gait cycle different between experimental and synthetic kinematics and GRFs), while a subset with narrow condition ranges was best replicated by the Multi-cGAN, producing similar kinematics (<1°) and GRFs (<0.02 body-weight) averaged by walking speeds. Multi-cGAN also generated synthetic datasets for three previous studies using reported mean and standard deviation of subject and gait characteristics. Additionally, unseen test data was best predicted by the walking speed-conditional, showcasing synthetic data diversity. The same model also matched the dynamical consistency of the experimental data (32 % average difference throughout the gait cycle), meaning that transforming the gait cycle data to the original time domain yielded accurate derivative calculations. Importantly, synthetic data poses no privacy concerns, potentially facilitating data sharing.

Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.

Seismic behavior and failure analysis of prefabricated foamed concrete composite walls for passive houses

In view of the lack of research on the failure mode and seismic behavior of high-performance insulation walls utilizing lightweight concrete as the structural material, this study presents experimental findings from a seismic behavior investigation of prefabricate foamed concrete composite walls (PFCCWs) for passive house in China. The PFCCWs incorporate low thermal conductivity and high-strength foamed concrete, polyurethane, decorative magnesium straw slabs to provide excellent thermal and mechanical properties for prefabricated passive houses in hot summer and cold winter area. The seismic behavior of PFCCWs under different influence factors were elaborated using quasi-static test data and observation of external damage. The lightweight and porous nature of foamed concrete leads to significant crack formation during failure. The findings suggest that the skin effect of magnesium straw slabs and polyurethane impedes crack development and improve the load-bearing capacity with ductility enhancement. Additionally, reducing the aspect ratio and substituting post-cast column concrete with common concrete are effective to improve the seismic behavior. The window opening changes the worst damage area of the wall and notably weakens the seismic behavior. The enhanced integrity of cast-in-place wall leads to the difference in the failure mechanism and increases in load-bearing and deformation capacities. Meanwhile, design suggestions for the foamed concrete insulation walls are also proposed by comparing the failure mode and hysteretic response of various wall types.

Failure behaviour of various rocks coated new thin spray-on liner with different curing time under uniaxial compression: Insights from AE and DIC

Thin spray-on liners (TSL) is increasingly popular in underground mine support operations. In this study, a new type of TSL material is proposed, and its macro-micro physical and mechanical properties are characterized. A systematic investigation on the deformability behaviour of six rocks (i.e., coal rock, soft red sandstone, marble, hard sandstone, granite, and limestone) covered TSL materials with different curing ages (i.e., 0-day, 3-day, 7-day, 14-day, 21-day, and 28-day) under uniaxial compression circumstance is performed using acoustic emission (AE) and digital image correlation (DIC) methods. The results show that TSL improves the peak strain of each rock sample, except hard sandstone. The impact of TSL on the peak compressive strength of coal rock, soft red sandstone, and marble is nearly negligible, while it diminishes the strength of hard sandstone, granite, and limestone. As the TSL ages, the peak strength and elastic modulus of limestone initially increase and then decrease, reaching the minimum at 14-day of age, with values 89.81MPa and 23.13GPa respectively. However, for other rock samples, the effect of TSL age is not significant. The AE signal produced inside the rock diminishes during the elastic-plastic stage, while the ratio of shear cracks increases upon failure of each rock specimen. The macroscopic failure modes of coal rock and limestone exhibit prominent axial splitting characteristics, whereas marble and hard sandstone undergo mixed failure processes involving shear, tension, and shear-tension. The test data highly correspond to the macroscopic fracture characteristics. The research findings can serve as guidance for TSL support in both soft and hard rock scenarios.