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Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the effectiveness of individual prediction methods. This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model (PSO-PIP), which incorporates a particle swarm optimization algorithm enhanced with dynamic clustering and adaptive parameter tuning (KGPSO). The model integrates multi-source data from the Lattice Boltzmann Method (LBM), Pore Network Modeling (PNM), and Finite Difference Method (FDM). By assigning optimal weight coefficients to the outputs of these methods, the model minimizes deviations from actual values and enhances permeability prediction performance. Initially, the computational performances of the LBM, PNM, and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples. It is observed that these methods exhibit computational biases in certain permeability ranges. The PSO-PIP model is proposed to combine the strengths of each computational approach and mitigate their limitations. The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals, significantly enhancing prediction accuracy. The outcomes of this study provide a new tool and perspective for the comprehensive, rapid, and accurate prediction of permeability in porous media.

Abstract Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and storage cost. Researchers have proposed practical acceleration solutions to address these issues. This paper aims to provide a comprehensive survey for systematically comparing and analyzing the strengths and weaknesses of FHE acceleration schemes, which is currently lacking in the literature. The relevant researches conducted between 2019 and 2022 are investigated. We first provide a comprehensive summary of the latest research findings on accelerating FHE, aiming to offer valuable insights for researchers interested in FHE acceleration. Secondly, we classify existing acceleration schemes from algorithmic and hardware perspectives. We also propose evaluation metrics and conduct a detailed comparison of various methods. Finally, our study presents the future research directions of FHE acceleration, and also offers both guidance and support for practical application and theoretical research in this field.

As inorganic rigid waterproof materials with excellent substrate compatibility, non-toxicity, harmless, and deep penetration depth, cementitious capillary crystalline waterproof materials (CCCW) hold great potential for application in concrete structures. However, the formulations of commercially available CCCW are currently undisclosed, hindering the mechanism study of CCCW, which is not conducive to the development of more efficient and cost-effective CCCW materials. In this study, a new formulation of CCCW active chemicals was developed, consisting of calcium carbonate, sodium silicate, glycine, EDTA-4Na, and sodium acetate. The effectiveness of sodium acetate as a high-quality impermeable admixture to CCCW was explored to enhance the impermeability of the substrate. Subsequently, the fundamental performance of the coatings was subjected to further examination. After application of the coating, the impermeability and strength of the substrate were enhanced, and the fist impermeability strength reached 1.5 MPa. Micro characteristics testing indicated that the coated sample had a denser microstructure, resulting in an increase of calcium carbonate crystals and hydrated calcium silicate in the pores. The harmful pores (100–3000 nm) were transformed into less harmful or harmless pores (< 100 nm). The durability tests indicated a 12.37 % drop in carbonation area over 14 days, with an approximately 30 % drop in the Coulomb electric flux of chloride ions.

Alkali-activated slag cement (AASC) has shown immense potential for application as a low-carbon and eco-friendly cementitious material. It’s crucial to modify the AASC with superhydrophobic property to obtain excellent durability performance. In this paper, low-cost and non-toxic stearic acid (SA) is selected as a hydrophobic agent to impart integral hydrophobic property to the alkali-activated Portland slag cement mortar (AAPSCM). The SA-modified AAPSCM (M-AAPSCM) shows superhydrophobicity with a contact angle of 150.6° and a sliding angle of 9.5°, even when hammered to reveal its broken inner structure. The characterization analysis indicates that stearic acid not only successfully grafts onto the mortar surface but also forms calcium stearate, which successfully reduces the surface energy of the material. Combined with the porous rough structure constructed by medium sand and ethanol volatilization, the originally hydrophilic material achieves hydrophobic modification under the dual synergistic effect. The M-AAPSCM reached a compressive strength of 20.8 MPa after 28 days of curing, satisfying the requirements for hydraulic engineering applications. And the M-AAPSCM not only exhibits an excellent self-cleaning ability against contaminants like sand particles and chalk powder, but also demonstrates remarkable mechanical robustness, even after being subjected to blade scratching or mechanical fracture. Concurrently, the initial and final setting times of M-AAPSCM were determined to be 32 minutes and 41 minutes respectively, demonstrating its rapid-setting characteristic. Additionally, following the hydrophobic modification, AAPSCM effectively overcomes its inherent issues of severe autogenous shrinkage and poor carbonation resistance. Furthermore, when compared with the ordinary AAPSCM (O-AAPSCM), the durability of the M-AAPSCM against dry-wet cycles, icing, and freeze-thaw cycles is significantly enhanced.

Migration-based Dynamic Platform (MdyPlat) is a type of Moving Target Defense (MTD) techniques to protect the task executed on the platform. MdyPlat actively hinders sophisticated attacks through randomly and dynamically setting up a platform for executing the task. This paper aims to investigate how MdyPlat-related factors make quantitative impact on the capability of MdyPlat technique. We develop a semi-Markov model for describing the system dynamics and then derive metric calculation formulas for investigation. Compared to the existing analytical-modeling-based evaluation methods, our modeling approach can work even the times of all MdyPlat-related events follow any type of distribution. The comparison between simulation and numerical results validates the approximate accuracy of the model and formulas. Numerical experiment results uncover that 1) MdyPlat technique can effectively enhance the task security, particularly under high attack intensity; 2) The security risk of the executed task varies in different task-execution scenarios, which are denoted by the number of platforms and the probability distribution of the times of MDP-related events; and 3) Different cumulative distribution function of event time under the same mean value leads to different analysis result of the task security.

IntroductionBlack African American (B/AA) women have a 2-fold to 3-fold elevated risk compared with non-Hispanic White (W) women for preterm birth. Further, preterm birth is the leading cause of mortality among B/AA infants, and among survivors, preterm infant adverse health outcomes occur disproportionately in B/AA infants. Racial inequities in maternal and infant health continue to pose a public health crisis despite the discovery >100 years ago. The purpose of this study was to expand on reported preterm infant outcome disparities. A life-course approach, accumulation of lifelong stress, including discrimination, may explain social factors causing preterm birth rate and outcome inequities in B/AA mothers.MethodsAnthropometric measures and clinical treatment information for 197 consented participants were milled from electronic health records across 4 years. The Neonatal Infant Stressor Scale was used to tally acute and chronic painful/stressful procedures. Neurobehavioral differences were investigated using the Neonatal Intensive Care Unit (NICU) Network Neurobehavioral Scale.ResultsB/AA mothers gave birth to preterm infants earlier than W mothers. NICU hospitalization stays were extended more than 2 weeks for the significantly smaller B/AA preterm infants in comparison to the age-matched W preterm infants. A higher number of chronic lifesaving procedures with demonstrated altered stress response patterns were recorded for B/AA preterm infants.DiscussionThis cross-sectional analysis of preterm birth rates and preterm infant developmental and neurodevelopmental outcomes are presented in the context of NICU stress and pain, with attendant implications for infant mortality and future health disparities. Preterm birth rate and outcome inequities further support the need to develop interventions and policies that will reduce the impact of discrimination and improve social determinants of health for Black, Indigenous, and other People of Color.

Abstract The continuous increase of electric vehicles is being facilitating the large-scale distributed charging-pile deployment. It is crucial to guarantee normal operation of charging piles, resulting in the importance of diagnosing charging-pile faults. The existing fault-diagnosis approaches were based on physical fault data like mechanical log data and sensor data streams. However, there are other types of fault data, which cannot be used for diagnosis by these existing approaches. This paper aims to fill this gap and consider 8 types of fault data for diagnosing, at least including physical installation error fault, charging-pile mechanical fault, charging-pile program fault, user personal fault, signal fault (offline), pile compatibility fault, charging platform fault, and other faults. We aim to find out how to combine existing feature-extraction and machine learning techniques to make the better diagnosis by conducting experiments on realistic dataset. 4 word embedding models are investigated for feature extraction of fault data, including N-gram, GloVe, Word2vec, and BERT. Moreover, we classify the word embedding results using 10 machine learning classifiers, including Random Forest (RF), Support Vector Machine, K-Nearest Neighbor, Multilayer Perceptron, Recurrent Neural Network, AdaBoost, Gradient Boosted Decision Tree, Decision Tree, Extra Tree, and VOTE. Compared with original fault record dataset, we utilize paraphrasing-based data augmentation method to improve the classification accuracy up to 10.40%. Our extensive experiment results reveal that RF classifier combining the GloVe embedding model achieves the best accuracy with acceptable training time. In addition, we discuss the interpretability of RF and GloVe.

Abstract Service function chain (SFC) based on network function virtualization (NFV) technology can handle network traffic flexibly and efficiently. The virtual network function (VNF), as the core function unit of SFC, can experience software aging, which reduces the availability and reliability of SFC and even leads to service interruption, after it runs continuously for a long time. Software rejuvenation technique can effectively combat software aging. However, its effectiveness in improving the availability and reliability of SFC needs to be evaluated. Compared with existing models, this paper proposes a semi-Markov model to capture the behaviors of each VNF in a SFC from the occurrence of software aging to recovery by software rejuvenation technique under the condition that the failure times and recovery times follow general distribution, while considering trigger intervals of software rejuvenation technique. We then derive the calculation formulas of the steady-state availability, transient availability, and reliability, which are applied to evaluate the effectiveness of software rejuvenation technique. Finally, we conduct sensitivity analysis and numerical experiments to analyze the effects of system parameters, the number of VNFs and trigger interval of software rejuvenation technique on availability and reliability of SFC, and the effects of time-varying parameters on transient availability of SFC.

High belite sulphoaluminate cement (HBSC) has shown great potential as new lower CO2 footprint cementitious material. It makes sense to improve the hydrophobic property of HBSC to obtain high durability performance. In this paper, nontoxic short carbon chain lauric acid (LA) is used to match the early strength characteristic of HBSC to prepare fast-setting and eco-friendly LA-modified HBSC mortar (M-HBSCM). Sands modified with LA provide low surface energy for an integral hydrophobic effect. M-HBSCM exhibits superhydrophobicity even after hammering to expose the fractured internal structure, with a contact angle of 153.2°. And it shows outstanding self-cleaning property against contaminants such as sand and wet mud and a reduction in water absorption to around 23% of the ordinary HBSCM (O-HBSCM). The fabricated M-HBSCM has fast-setting property with an initial setting time of 29 min and a final setting time of 36 min. The abrasion test displays that the scratched area remains well superhydrophobic with high mechanical robustness when the droplets roll off on the inclined surface. Field-emission scanning electron microscope (FESEM) indicates that M-HBSCM contains more pores and coarser hydration products. The system of calcium alumina and pores exhibits a distinct hierarchical structure, providing the multilevel roughness required for the superhydrophobic surface. Fourier transform infrared (FTIR) spectrometer indicates that low surface energy functional groups have been successfully grafted onto M-HBSCM. The superhydrophobic concrete has good mechanical robustness, corrosion resistance, and fast-setting property, which can meet the demands of grouting reinforcement projects, the repair of various concrete structures, corrosion protection of marine structures and other projects.

Background Poor sleep raises the risk of non-alcoholic fatty liver disease (NAFLD) and hastens disease progression. It is critical to figure out what factors impact the sleep quality of NAFLD patients. The present study aimed to investigate the role of anxiety symptoms in accounting for the impact of rumination on sleep quality and the moderating role of resilience on the associations of rumination with anxiety symptoms and sleep quality. Methods In the cross-sectional study, 285 NAFLD patients completed the Chinese version of the Pittsburgh Sleep Quality Index, the Ruminative Responses Scale, the Generalized Anxiety Disorder 7-item scale, and the 14-item Resilience Scale to measure sleep quality, rumination (including brooding and reflection), anxiety symptoms, and resilience, respectively. The PROCESS macro for SPSS v4.0 procedure was applied to perform moderated mediation analysis. Results The roles of anxiety symptoms in accounting for the positive associations of brooding, reflection and rumination with poor sleep quality were revealed. It was found that there was a significant moderating role of resilience on the positive associations of brooding, reflection and rumination with anxiety symptoms, which were gradually reduced as resilience increased. The direct associations between brooding, reflection and rumination and poor sleep quality were not significantly moderated by resilience. Thus, a moderated mediation model involving anxiety symptoms and resilience for explaining the impact of rumination on poor sleep quality was supported among patients with NAFLD. Conclusions Rumination (including brooding and reflection) could be positively related to poor sleep quality, and anxiety symptoms had a significant role in accounting for the relationship among patients with NAFLD. Resilience showed a moderating role that could attenuate the positive association between rumination and anxiety symptoms. Interventions aimed at alleviating rumination, reducing anxiety symptoms, and enhancing resilience could improve the sleep quality of NAFLD patients.

OBJECTIVES/GOALS: Early life pain/stress impacts infants’ neurodevelopmental outcomes. Mitochondrial dysfunction may interface between infants’ stress and neurodevelopment. The study aims to investigate the associations between pain/stress, proteins associated with mitochondrial dysfunction, and neurobehavioral responses in preterm infants. METHODS/STUDY POPULATION: A prospective cohort study was conducted with 33 preterm infants enrolled between September 2017 and July 2022 at two affiliated NICUs in Hartford and Farmington, CT. Daily pain/stress experienced during NICU was documented. At 36-38 weeks post-menstrual age (PMA), neurobehavioral outcomes were evaluated using the NICU Network Neurobehavioral Scale (NNNS) and buccal swabs for Mass spectrometry-based proteomics analysis. Lasso statistical methods were conducted to study the association between protein abundance and infants’ NNNS summary scores. Multiple linear regression and Gene Ontology (GO) enrichment analyses were performed to examine how clinical characteristics and neurodevelopmental outcomes may be associated with protein levels and underlying molecular pathways. RESULTS/ANTICIPATED RESULTS: During NICU hospitalization, preterm premature rupture of membrane (PPROM) was negatively associated with neurobehavioral outcomes. The protein functions, including leptin receptor binding activity, glutathione disulfide oxidoreductase activity, and response to oxidative stress, lipid metabolism, phosphate, and proton transmembrane transporter activity, were negatively associated with neurobehavioral outcomes. In contrast, cytoskeletal regulation, epithelial barrier, and protection function were found to be positively associated with neurodevelopmental outcomes. In addition, mitochondrial dysfunction-related proteins (SPRR2A, PAIP1, S100A3, MT-CO2, PiC, GLRX, PHB2, and BNIPL-2, ABLIM1, UNC45A, Keratins, MUC1, and CYB5B) were found to be associated with neurobehavioral outcomes. DISCUSSION/SIGNIFICANCE: Mitochondrial dysfunction-related proteins were observed to be associated with early life pain/stress and neurodevelopmental outcomes in infants. Buccal proteins could be used to predict potential neurobehavioral outcomes. In addition, individualized skin integrity protection should be provided to preterm infants during their NICU stay.

As an indispensable part of intelligent transportation system, a traffic-sensitive navigation system can assist drivers in avoiding traffic congestion by providing navigation services. The navigation service provider (NSP) utilizes road condition information from nearby vehicles collected by roadside units (RSUs) to guide a vehicle through an optimal route provided by the navigation services. This paper mainly focuses on tackling three key issues in such navigation system. Firstly, it is essential to ensure the privacy of the vehicles’ personal identifiable information (PII). Secondly, the location, start point destination and route of the vehicle should not be leaked to RSUs and also should not be linked to its PII by RSUs and NSP. Thirdly, the process of vehicles obtaining the navigation services should be lightweight. Because of such problems, this paper proposes a dual privacy-preserving lightweight navigation system for vehicular networks through designing a novel signature scheme and combining other cryptographic primitives to keep dual privacy including PII and route related information. And the correctness analysis of the proposed system, security proof of the designed signature scheme adopted in the proposed system, and privacy analysis of the system are thoroughly provided. In addition, the performance of the proposed system is evaluated and compared with the existing systems to illustrate that the proposed system is efficient.

With the continuous development of electric vehicles (EV), large-scale distributed charging piles have been deployed in the wild. Therefore, it is extremely essential to evaluate the risk state of EV charging piles efficiently and effectively. This paper aims to measure the capability of supervised and semi-supervised machine learning techniques in assessing the risk state of EV charging piles. We investigate 8 algorithms, including Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Self-Training based on SVM (ST-SVM), Self-Training based on RF (ST-RF), Self-Training based on AdaBoost (ST-AdaBoost) and Self-Training based on GBDT (ST-GBDT). We first collect data on normal and abnormal termination of charging services from an actual Internet of Vehicles platform. The dataset consists of 17,773 recordings and 7 features generated from the records, which are used for classification. According to the statistical times of 7 features, 20% of recordings are labeled by knowledgable experts into three classes: low-risk, medium-risk and high-risk. Experimental results indicate that ST-AdaBoost and ST-GBDT show more excellent overall classification performance, compared with the other traditional supervised methods. We also apply ST-GBDT to predict the risk state of the unclassified piles and produce the statistic of piles from different manufacturers.

Abstract Adversarial Malware Example (AME)-based adversarial training can effectively enhance the robustness of Machine Learning (ML)-based malware detectors against AME. AME quality is a key factor to the robustness enhancement. Generative Adversarial Network (GAN) is a kind of AME generation method, but the existing GAN-based AME generation methods have the issues of inadequate optimization, mode collapse and training instability. In this paper, we propose a novel approach (denote as LSGAN-AT) to enhance ML-based malware detector robustness against Adversarial Examples, which includes LSGAN module and AT module. LSGAN module can generate more effective and smoother AME by utilizing brand-new network structures and Least Square (LS) loss to optimize boundary samples. AT module makes adversarial training using AME generated by LSGAN to generate ML-based Robust Malware Detector (RMD). Extensive experiment results validate the better transferability of AME in terms of attacking 6 ML detectors and the RMD transferability in terms of resisting the MalGAN black-box attack. The results also verify the performance of the generated RMD in the recognition rate of AME.

Recently, researchers are being more interested in performing operations directly on the encrypted database with the help of the $O$ rder- $P$ reserving $E$ ncryption (OPE) scheme. This mechanism enables executing many types of queries efficiently, such as range query and comparison, since it can preserve the relative order of underlying plaintext on ciphertexts. However, traditional OPE schemes cannot achieve ideal security against IND-OCPA (IND istinguishability under $O$ rdered $C$ hosen- $P$ laintext $A$ ttack) in a linear length of static encoding. Popa’s mutable scheme (namely, mOPE) is an effective solution to perform the range query in the database environment. In this paper, we propose a novel scheme, $B$ oundary $O$ rder- $P$ reserving $E$ ncryption (BOPE), to achieve high performance under ideal security. BOPE comprises two algorithms. One is a searching algorithm in which we propose a data structure, the boundary tree, to optimize the algorithm by cutting the scope of each iteration and reducing the rounds of interaction. The second algorithm is an updating algorithm for stale encoding, which determines whether to update the lookup table according to the type of each query, in order to avoid the time cost of redundant updates. We implemented and evaluated BOPE on a practical environment where we have achieved a performance increase of more than 10% from mOPE.

Abstract Deep learning (DL) has exhibited its exceptional performance in fields like intrusion detection. Various augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL models. However, the classic augmentation methods cannot be applied to those DL models which exploit the system-call sequences to detect intrusion. Previously, the seq2seq model has been explored to augment system-call sequences. Following this work, we propose a gated convolutional neural network (GCNN) model to thoroughly extract the potential information of augmented sequences. Also, in order to enhance the model’s robustness, we adopt adversarial training to reduce the impact of adversarial examples on the model. Adversarial examples used in adversarial training are generated by the proposed adversarial sequence generation algorithm. The experimental results on different verified models show that GCNN model can better obtain the potential information of the augmented data and achieve the best performance. Furthermore, GCNN with adversarial training can enhance robustness significantly.

The fifth generation (5G) technology offers a set of security services in order to provide secure communication to a large number of various devices. 5G Authentication and Key Agreement (5G-AKA) authentication service acts as the first step of securing the 5G network communication. This paper aims to quantitatively analyze the dependability of 5G-AKA authentication service. We develop the continuous-time Markov chain (CTMC) models to capture the operational details of the 5G-AKA authentication services when facing the service failure. We also derive formulas for calculating metrics of interest, including Defects Per Million (DPM), authentication service's first restoration time from failure and total cost of ownership (TCO). Extensive numerical analysis is applied to investigate the impact of parameters on the evaluation metrics.

Over the last few years, the malware propagation on PC platforms, especially on Windows OS has been even severe. For the purpose of resisting a large scale of malware variants, machine learning (ML) classifiers for malicious Portable Executable (PE) files have been proposed to achieve automated classification. Recently, function call graph (FCG) vectorization (FCGV) representation was explored as the input feature to achieve higher ML classification accuracy, but FCGV representation loses some critical features of PE files due to the hash technique. This paper aims to further improve the classification accuracy of FCGV-based ML model by applying both graph and non-graph features. We propose an FCGV-SF based Random Forest classification model, which applies both FCGV features (graph features) and statistical features (SF, non-graph features) extracted from disassembled PE files. Six types of effective non-graph features are chosen for our integrated vector, namely, metadata, symbol, operation code, register, section and data definition. We evaluate our model on a dataset provided by Microsoft hosted at Kaggle, and the experimental results indicate that the classification accuracy increases from 0.9851 to 0.9957 compared with the existing model based on FCGV only.

Virtualization technology has promoted the fast development and deployment of cloud computing, and is now becoming an enabler of Internet of Everything. Virtual machine monitor (VMM), playing a critical role in a virtualized system, is software and hence it suffers from software aging after a long continuous running as well as software crashes due to elusive faults. Software rejuvenation techniques can be adopted to reduce the impact of software aging. Although there existed analytical model-based approaches for evaluating software rejuvenation techniques, none analyzed both application service (AS) availability and job completion time in a virtualized system with live virtual machine (VM) migration. This paper aims to quantitatively analyze software rejuvenation techniques from service provider and user views in a virtualized system deploying VMM reboot and live VM migration techniques for rejuvenation, under the condition that all the aging time, failure time, VMM fixing time and live VM migration time follow general distributions. We construct an analytical model by using a semi-Markov process (SMP) and derive formulas for calculating AS availability and job completion time. By analytical experiments, we can obtain the optimal migration trigger intervals for achieving the approximate maximum AS availability and the approximate minimum job completion time, and then service providers can make decisions for maximizing the benefits of service providers and users by adjusting parameter values.

Moving target defense (MTD) has recently emerged as a game-changer in the confrontation between cyberattack and defense. MTD mechanism constantly and randomly changes the system configurations to create uncertainty of the attack surface against cyber-adversaries. To date, researches on the evaluation of MTD techniques either focused on analyzing the effectiveness of MTD or studying the system performance loss due to the use of MTD. The impact on job/service running on the protected system is always ignored. In this paper, we propose an SRN (Stochastic reward net) based analytical modeling approach to investigate how MTD techniques influence the job running on protected system from the perspective of job finish time. The SRN model developed in this paper captures the behaviors of both the adversary and the job execution process. Furthermore, we carry out numerical analysis to study the impact of different system parameters on job finish time and other evaluation metrics. The results in this paper can help defenders choose a better MTD configuration to complete the job execution as soon as possible.

Deep Neural Networks (DNNs) have been gaining state-of-the-art achievement compared with many traditional Machine Learning (ML) models in diverse fields. However, adversarial examples challenge the further deployment and application of DNNs. Analysis has been carried out for studying the reasons of DNNs' vulnerability to adversarial perturbation and focused on model architecture. No research has been done on investigating the impact of optimization algorithms (namely, optimizers in DNNs) employed in training DNN models on models' sensitivity to adversarial examples. This paper aims to study this impact from an experimental perspective. We analyze the sensitivity of a model not only from the aspect of white-box and black-box attack setups, but also from the aspect of different types of datasets. Four common optimizers, SGD, RMSprop, Adadelta, and Adam, are investigated on structured and unstructured datasets. Extensive experiment results indicate that an optimization algorithm does pose effects on the DNN model sensitivity to adversarial examples. That is, when training models and generating adversarial examples, Adam optimizer can generate better quality adversarial examples for structured datasets, and Adadelta optimizer can generate better quality adversarial examples for unstructured datasets. In addition, the choice of optimizers does not affect the transferability of adversarial examples.

The influence of glass powder (GP) on hydration kinetics of composite cementitious materials has been investigated by isothermal calorimetry test and hydration kinetics methods in this paper. The hydration heat emission rate and hydration heat decrease gradually while the induction and acceleration period increase with the increase of GP content. According to Krstulovic-Dabic model, the hydration process of composite cementitious materials containing GP is controlled by a variety of complicated reaction mechanisms, which can be divided into three periods: nucleation and crystal growth (NG), phase boundary reaction (I), and diffusion (D). The NG and I process are shortened after incorporating GP.

How to accurately determine the mechanical parameters of rockfill is one of the key issues of concrete-face rockfill dams. Parameter back-analysis using internal or external monitoring data has been proven to be an efficient way to solve this problem. However, traditional internal or external monitoring methods have limitations in efficiency and long-term monitoring. In this paper, the displacement of the Shuibuya concrete-face rockfill dam is monitored by the space-borne Interferometric Synthetic Aperture Radar (InSAR) time series method. Using the InSAR results and the finite element method, the back-analysis of the mechanical parameters of the rockfill dam is investigated, and the back-analysis results of InSAR and levelling are compared. A high correlation of 0.99 for the displacement results generated from InSAR and the levelling offers good agreement between the two methods. The agreement provides confidence that the external InSAR monitoring measurement allows producing a reliable back-analysis and captures the displacement properties of the dam. Based on the identified parameters from the InSAR results, the dam displacement is predicted. The prediction of the maximum settlement of the dam is 2.332 m by the end of 2020, according to the dam displacement characteristics, which agrees well with the results derived from the recorded internal monitoring data. Therefore, the external monitoring results from the InSAR observation can be used as a supplement for traditional monitoring methods to analyse the parameters of the dam.

Settlement is one of the most important deformation characteristics of high concrete faced rockfill dams (CFRDs, >100 m). High CFRDs safety would pose a great threat to the security of people’s lives and property downstream if this kind of deformation were not to be measured correctly, as traditional monitoring approaches have limitations in terms of durability, coverage, and efficiency. It has become urgent to develop new monitoring techniques to complement or replace traditional monitoring approaches for monitoring the safety and operation status of high CFRDs. This study examines the Shuibuya Dam (up to 233.5 m in height) in China, which is currently the highest CFRD in the world. We used space-borne Interferometric Synthetic Aperture Radar (InSAR) time series to monitor the surface deformation of the Shuibuya Dam. Twenty-one ALOS PALSAR images that span the period from 28 February 2007 to 11 March 2011 were used to map the spatial and temporal deformation of the dam. A high correlation of 0.93 between the InSAR and the in-situ monitoring results confirmed the reliability of the InSAR method; the deformation history derived from InSAR is also consistent with the in-situ settlement monitoring system. In addition, the InSAR results allow continuous investigation of dam deformation over a wide area that includes the entire dam surface as well as the surrounding area, offering a clear picture continuously of the dam deformation.