Regularity analysis of resilient modulus for hot-mix asphalt with large temperature fluctuations

To evaluate the regularity of resilient modulus for hot-mix asphalt(HMA)under large temperature fluctuations,back propagation(BP)neural network technology was used to analyze the continuous change of HMA resilient modulus.Firstly,based on the abundant data,the training model of HMA resilient modulus was established by using BP neural network technology.Subsequently,BP neural network prediction and regression analysis were performed,and the prediction model of HMA resilient modulus at different temperatures(50C to 60C)was obtained,which fully considered multi-factor and nonlinearity.Finally,the fitted theoretical model can be used to evaluate the HMA performance under the condition of large temperature fluctuations,and the rationality of theoretical model was verified by taking Harbin region as an example.It was found that the relationship between HMA resilient modulus and temperatures can be described by inverse tangent function.And the key parameters of theoretical model can be used to evaluate the continuous change characteristics of HMA resilient modulus with large temperature fluctuations.The results can further improve the HMA performance evaluation system and have certain theoretical value.

Modelling of the variation of granular base materials resilient modulus with material characteristics and humidity conditions

This study aims to quantify the susceptibility of granular materials used in pavements to changes in moisture content and propose a correlation model to incorporate this susceptibility into seasonal analyses.The fines content and the percentage of fractured coarse aggregates were identified as direct indicators of the resilient modulus susceptibility to changes in water content.The results showed that the percentage of fractured coarse aggregates particles(FR)has a more significant impact on the resilient modulus(Er)of crushed granular materials used in pavement construction than the combined indicator of the fines content and sample volumetrics(nf).Crushed granular materials with a higher percentage of fractured coarse aggregates are relatively insensitive to changes in the degree of saturation,but become more sensitive as the fine fraction porosity decreases.An adjusted model was proposed based on the existing formulation,but considers a complex parameter to describe and adjust the sensitivity of base granular materials to variations in moisture content with respect to fabrication charac-teristics,fines content and volumetric properties.The model shows that the variation of Er values is below10%for fully crushed granular materials.However,it reaches approximately±12%for materials with 75%of crushed coarse aggregates andþ40%and-25%for materials with FR=50%.This model could help select good ag-gregates characteristics and adjust grain-size distribution for environments where significant moisture content variations can occur in the pavement system,such as in the Province of Quebec(Canada).As it is based on pa-rameters that can be easily determined or estimated,it also represents a valuable tool for detailed design and analysis that can consider material characteristics.

Strength and elastic modulus enhancement in Mg-Li-Al matrix composites reinforced by ex situ TiB2 particles via stir casting

A novel Mg^(-1)0Li-3Al(wt.%,LA103)matrix composite reinforced by ex situ micron TiB_(2) particles was developed in the present study.The ball milling and cold pressing pretreatment of the reinforcements made it feasible to prepare this material under stir casting conditions with good dispersion.The microstructure and mechanical properties of the composites prepared by different pretreatment methods were analyzed in detail.The TiB_(2) particles in the Al-TiB_(2)/LA103 composite using the pretreatment process were uniformly distributed in the microstructure due to the formation of highly wettable core-shell units in the melt.Compared with the matrix alloys,the Al-TiB_(2)/LA103 composite exhibited effective strength and elastic modulus improvements while maintaining acceptable elongation.The strengthening effect in the composites was mainly attributed to the strong grain refining effect of TiB2.This work shows a balance of high specific modulus(36.1 GPa·cm^(3)·g^(-1))and elongation(8.4%)with the conventional stir casting path,which is of considerable application value.

A novel relationship between elastic modulus and void ratio associated with principal stress for coral calcareous sand

Elastic moduli,e.g.shear modulus G and bulk modulus K,are important parameters of geotechnical materials,which are not only the indices for the evaluation of the deformation ability of soils but also the important basic parameters for the development of the constitutive models of geotechnical materials.In this study,a series of triaxial loading-unloading-reloading shear tests and isotropic loading-unloadingreloading tests are conducted to study several typical mechanical properties of coral calcareous sand(CCS),and the void ratio evolution during loading,unloading and reloading.The test results show that the stress-strain curves during multiple unloading processes are almost parallel,and their slopes are much greater than the deformation modulus at the initial stage of loading.The relationship between the confining pressure and the volumetric strain can be defined approximately by a hyperbolic equation under the condition of monotonic loading of confining pressure.Under the condition of confining pressure unloading,the evolution of void ratio is linear in the e-lnp0 plane,and these lines are a series of almost parallel lines if there are multiple processes of unloading.Based on the experimental results,it is found that the modified Hardin formulae for the elastic modulus estimation have a significant deviation from the tested values for CCS.Based on the experimental results,it is proposed that the elastic modulus of soils should be determined by the intersection line of two spatial surfaces in the G/K-e-p’/pa space(pa:atmosphere pressure).“Ye formulation”is further proposed for the estimation of the elastic modulus of CCS.This new estimation formulation for soil elastic modulus would provide a new method to accurately describe the mechanical behavior of granular soils.

Effects of Initial Defects on Effective Elastic Modulus of Concrete with Mesostructure

An exquisite mesostructure model was presented to predict the effective elastic modulus of concrete,in which concrete is realized as a four-phase composite material consisting of coarse aggregates,mortar matrix,interfacial transition zone(ITZ),and initial defects.With the three-dimensional(3D)finite element(FE)simulation,the highly heterogeneous composite elastic behavior of concrete was modeled,and the predicted results were compared with theoretical estimations for validation.Monte Carlo(MC)simulations were performed with the proposed mesostructure model to investigate the various factors of initial defects influencing the elastic modulus of concrete,such as the shape and concentration(pore volume fraction or crack density)of microspores and microcracks.It is found that the effective elastic modulus of concrete decreases with the increase of initial defects concentration,while the distribution and shape characteristics also exert certain influences due to the stress concentration caused by irregular inclusion shape.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

Numerical Analysis for High⁃Throughput Elastic Modulus Measurement of Substrate⁃Supported Thin Films

Micro/nano-thin films are widely used in the fields of micro/nano-electromechanical system(MEMS/NEMS)and flexible electronics,and their mechanical properties have an important impact on the stability and reliability of components.However,accurate characterization of the mechanical properties of thin films still faces challenges due to the complexity of film-substrate structure,and the characterization efficiency of traditional techniques is insufficient.In this paper,a high-throughput determination method of the elastic modulus of thin films is proposed based on the strain variance method,the feasibility of which is analyzed by the finite element method(FEM),and the specific tensile configuration with array-distributed thin films is designed and optimized.Based on the strain difference between the film-substrate region and the uncoated region,the elastic modulus of multiple films is obtained simultaneously,and the influences of film width,spacing,thickness,and distribution on the measurement of elastic modulus are elucidated.The results show that the change in film width has a more obvious effect on the elastic modulus determination than film spacing and thickness,i.e.,the larger the film width is,the closer the calculation results are to the theoretical value,and the change in calculation results tends to be stabilized when the film width increases to a certain length.Specifically,the simultaneous measurement of the elastic modulus of eight metal films on a polyimide(PI)substrate with a length of 110 mm and a width of 30 mm can be realized,and the testing throughput can be further increased with the extension of the substrate length.This study provides an efficient and low-cost method for measuring the elastic modulus of thin films,which is expected to accelerate the development of new thin film materials.

High-modulus solid electrolyte interphase layer with gradient composition enables long-cycle all-solid-state lithium-sulfur batteries

All-solid-state lithium-sulfur batteries(ASSLSBs) have become one of the most potential candidates for the next-generation high-energy systems due to their intrinsic safety and high theoretical energy density.However, PEO-based ASSLSBs face the dilemma of insufficient Coulombic efficiency and long-term stability caused by the coupling problems of dendrite growth of anode and polysulfide shuttle of cathode. In this work, 1,3,5-trioxane(TOX) is used as a functional additive to design a PEO-based composite solidstate electrolyte(denoted as TOX-CSE), which realizes the stable long-term cycle of an ASSLSB. The results show that TOX can in-situ decompose on the anode to form a composite solid electrolyte interphase(SEI) layer with rich-organic component. It yields a high average modulus of 5.0 GPa, greatly improving the mechanical stability of the SEI layer and thus inhibiting the growth of dendrites. Also,the robust SEI layer can act as a barrier to block the side reaction between polysulfides and lithium metal.As a result, a Li-Li symmetric cell assembled with a TOX-CSE exhibits prolonged cycling stability over 2000 h at 0.2 m A cm^(-2). The ASSLSB also shows a stable cycling performance of 500 cycles at 0.5 C.This work reveals the structure–activity relationship between the mechanical property of interface layer and the battery's cycling stability.

Interconnected microstructure and flexural behavior of Ti_(2)C-Ti composites with superior Young’s modulus

To enhance the Young’s modulus(E)and strength of titanium alloys,we designed titanium matrix composites with intercon-nected microstructure based on the Hashin-Shtrikman theory.According to the results,the in-situ reaction yielded an interconnected microstructure composed of Ti_(2)C particles when the Ti_(2)C content reached 50vol%.With widths of 10 and 230 nm,the intraparticle Ti lamellae in the prepared composite exhibited a bimodal size distribution due to precipitation and the unreacted Ti phase within the grown Ti_(2)C particles.The composites with interconnected microstructure attained superior properties,including E of 174.3 GPa and ultimate flexural strength of 1014 GPa.Compared with that of pure Ti,the E of the composite was increased by 55% due to the high Ti_(2)C content and interconnected microstructure.The outstanding strength resulted from the strong interfacial bonding,load-bearing capacity of interconnected Ti_(2)C particles,and bimodal intraparticle Ti lamellae,which minimized the average crack driving force.Interrupted flexural tests revealed preferential crack initiation along the{001}cleavage plane and grain boundary of Ti_(2)C in the region with the highest tensile stress.In addition,the propagation can be efficiently inhibited by interparticle Ti grains,which prevented the brittle fracture of the composites.

Resilient water quality management: Insights from Japan's environmental quality standards for conserving aquatic life framework

Currently,chemicals and waste are recognized as key drivers of habitat degradation and biodiversity loss in aquatic ecosystems.To ensure vibrant habitats for aquatic species and maintain a sustainable aquatic food supply system,Japan promulgated its Environmental Quality Standards for the Conservation of Aquatic Life(EQS-CAL),based on its own aquatic life water quality criteria(ALWQC)derivation method and application mechanism.Here we overview Japan's EQS-CAL framework and highlight their best practices by examining the framework systems and related policies.Key experiences from Japan's EQS-CAL system include:(1)Classifying six types of aquatic organisms according to their adaptability to habitat status;(2)Using a risk-based chemical screening system for three groups of chemical pollutants;(3)Recommending a five-step method for determining ALWQC values based on the most sensitive life stage of the most sensitive species;(4)Applying site-specific implementation mechanisms through a series of Plan-Do-Check-Act loops.This paper offers scientific references for other jurisdictions,aiding in the development of more resilient ALWQC systems that can maintain healthy environments for aquatic life and potentially mitigate ongoing threats to human societies and global aquatic biodiversity.

Enhanced asphalt dynamic modulus prediction: A detailed analysis of artificial hummingbird algorithm-optimised boosted trees

This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt concrete.Using a substantial dataset from NCHRP Report-547,the model was trained and rigorously tested.Performance metrics,specifically RMSE,MAE,and R2,were employed to assess the model's predictive accuracy,robustness,and generalisability.When benchmarked against well-established models like support vector machines(SVM)and gaussian process regression(GPR),the AHA-boosted model demonstrated enhanced performance.It achieved R2 values of 0.997 in training and 0.974 in testing,using the traditional Witczak NCHRP 1-40D model inputs.Incorporating features such as test temperature,frequency,and asphalt content led to a 1.23%increase in the test R2,signifying an improvement in the model's accuracy.The study also explored feature importance and sensitivity through SHAP and permutation importance plots,highlighting binder complex modulus|G*|as a key predictor.Although the AHA-boosted model shows promise,a slight decrease in R2 from training to testing indicates a need for further validation.Overall,this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete,making it a valuable asset for pavement engineering.

Renewal Needs and Solutions for Urban Old Communities under the Guidance of Resilient Security

The renewal strategy guided by resilient security aims to enhance the community’s resistance and resilience to natural disasters and other emergency situations,ensuring that the community can quickly restore normal functions in the face of shocks.By enhancing the resilience of the community,residents’sense of security and satisfaction can be improved,while promoting the long-term stability and prosperity of the city.Using methods such as literature review,case analysis,and demand investigation,this paper investigates the needs and strategies for the renewal of urban old communities,and explores the demand analysis and solutions for the renewal of urban old communities under the guidance of resilient security.This paper aims to address the lack of resilience in old communities caused by outdated construction,aging infrastructure,and insufficient public services,as well as how to improve the adaptability,resilience,and transformation ability of communities through renovation and transformation.

Principles for Designing New Types of Housing for a Strong and Resilient Local Economy in the Republic of Congo

The Congolese population is organised into households, which are thus headed by a chief who ensures the social well-being, development and integration into working life of the individuals in his charge. This study examines the functional principles of new housing design as an instrument for transforming the (current) failing economy into a strong and resilient one. Accordingly, a literature review of the practice of designing and building housing in human settlements in the Congo revealed the state of the art on this subject. An analysis of the existing housing stock from a demographic, social and economic point of view made it possible to identify the most common household sizes and numbers, as well as the lifestyle processes that determine the need for developed space. To this end, the experimental method was used to propose configuration plans for various new types of dwelling. To this end, the study highlighted the link between people’s standard of living and the effectiveness of their involvement in the local economy. To achieve the aims of the National Development Plan (NDP), particular attention must be paid to solving the housing problem. The existing housing stock actively contributes to the problems associated with unemployment and insecurity. Functional principles for the design of new types of housing have been developed. Four model types are proposed in line with the demographic structure of the population, their socio-economic characteristics and their lifestyle.

Prediction of constrained modulus for granular soil using 3D discrete element method and convolutional neural networks

To efficiently predict the mechanical parameters of granular soil based on its random micro-structure,this study proposed a novel approach combining numerical simulation and machine learning algorithms.Initially,3500 simulations of one-dimensional compression tests on coarse-grained sand using the three-dimensional(3D)discrete element method(DEM)were conducted to construct a database.In this process,the positions of the particles were randomly altered,and the particle assemblages changed.Interestingly,besides confirming the influence of particle size distribution parameters,the stress-strain curves differed despite an identical gradation size statistic when the particle position varied.Subsequently,the obtained data were partitioned into training,validation,and testing datasets at a 7:2:1 ratio.To convert the DEM model into a multi-dimensional matrix that computers can recognize,the 3D DEM models were first sliced to extract multi-layer two-dimensional(2D)cross-sectional data.Redundant information was then eliminated via gray processing,and the data were stacked to form a new 3D matrix representing the granular soil’s fabric.Subsequently,utilizing the Python language and Pytorch framework,a 3D convolutional neural networks(CNNs)model was developed to establish the relationship between the constrained modulus obtained from DEM simulations and the soil’s fabric.The mean squared error(MSE)function was utilized to assess the loss value during the training process.When the learning rate(LR)fell within the range of 10-5e10-1,and the batch sizes(BSs)were 4,8,16,32,and 64,the loss value stabilized after 100 training epochs in the training and validation dataset.For BS?32 and LR?10-3,the loss reached a minimum.In the testing set,a comparative evaluation of the predicted constrained modulus from the 3D CNNs versus the simulated modulus obtained via DEM reveals a minimum mean absolute percentage error(MAPE)of 4.43%under the optimized condition,demonstrating the accuracy of this approach.Thus,by combining DEM and CNNs,the variation of soil’s mechanical characteristics related to its random fabric would be efficiently evaluated by directly tracking the particle assemblages.

Resilient performance of self-centering hybrid rocking walls with curved interface under pseudo-static loading

Frame and rocking wall(FRW)structures have excellent resilient performance during earthquakes.However,the concrete at interfacial corners of rocking walls(RWs)is easily crushed due to local extreme compression during the rocking process.An innovative RW with a curved interface is proposed to prevent interfacial corners from producing local damage,enhancing its earthquake resilient performance(ERP).The precast wall panel with a curved interface is assembled into an integral self-centering hybrid rocking wall(SCRW)by two post-tensioned unbonded prestressed tendons.Moreover,two ordinary energy dissipation steel rebars and two shear reinforcements are arranged to increase the energy dissipation capacity and lateral resistance.Two SCRW specimens and one monolithic reinforced concrete(RC)shear wall(SW)were tested under pseudo-static loading to compare the ERPs of the proposed SCRW and the SW,focusing on studying the effect of the curved interface on the SCRW.The key resilient performance of rocking effects,failure modes,and hysteretic properties of the SCRW were explored.The results show that nonlinear deformations of the SCRW are concentrated along the interface between the SCRW and the foundation,avoiding damage within the SCRW.The restoring force provided by the prestressed tendons can effectively realize self-centering capacity with small residual deformation,and the resilient performance of the SCRW is better than that of monolithic SW.In addition,the curved interface of the SCRW makes the rocking center change and move inward,partially relieving the stress concentration and crush of concrete.The rocking range of the rocking center is about 41.4%of the width of the SCRW.

Artificial Intelligence Based on Resilient Leadership in the Health Sector-A Secondary Publication

At present,it is impossible to deny the existence of artificial intelligence in various areas of social life,understood as the simulation of expert human intelligence from computer processes that involve learning,reasoning,and self-correction,its benefits to the medical field,in particular,are innumerable,but their incorporation into health systems has been gradual for many reasons.According to the above,this research analyzed artificial intelligence based on resilient leadership in the health sector,for which qualitative research was carried out with a documentary-bibliographic design with printed and electronic documentary sources with theoretical contributions fromÁvila,Mayer,and Quesada[1],Morgan[2],Villa[3],and Finol[4],among others.It is highlighted that resilient leadership has become a strategic factor in all organizations,since times of uncertainty and changes lead institutions to properly manage the incorporation of technologies specifically AI,achieving in this way that the centers and professionals in the field of health assume the needs of the contexts and the innovations of the same.It is concluded that resilient leadership will allow artificial intelligence in the health sector to generate higher levels of learning and adaptability to the transformations that are necessary,whose resistance would make its application difficult and in the long run it will leave behind professionals who refuse to assume the contributions of these innovative techniques in medical practice.

Experimental study of dynamic resilient modulus of subgrade soils under coupling of freeze–thaw cycles and dynamic load

Although the dynamic properties of subgrade soils in seasonally frozen areas have already been studied, few researchers have considered the influence of shallow groundwater during the freeze–thaw(F–T) cycles. So a multifunctional F–T cycle system was developed to imitate the groundwater recharge in the subgrade during the freezing process and a large number of dynamic triaxial experiments were conducted after the F–T cycles. Some significant factors including the F–T cycle number, compaction degree, confining pressure, cyclic deviator stress, loading frequency, and water content were investigated for the resilient modulus of soils. The experimental results indicated that the dynamic resilient modulus of the subgrade was negatively correlated with the cyclic deviator stress, F–T cycle number, and initial water content, whereas the degree of compaction, confining pressure, and loading frequency could enhance the resilient modulus. Furthermore, a modified model considering the F–T cycle number and stress state was established to predict the dynamic resilient modulus. The calculated results of this modified model were very close to the experimental results. Consequently, calculation of the resilient modulus for F–T cycles considering the dynamic load was appropriate. This study provides reference for research focusing on F–T cycles with groundwater supply and the dynamic resilient moduli of subgrade soils in seasonally frozen areas.

Prediction of resilient modulus for subgrade soils based on ANN approach

The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.

Enhancement of resilient modulus of cohesive soil using an enzymatic preparation

The current study aims to evaluate the dynamic response of stabilized cohesive soil using an enzymatic preparation in terms of resilient modulus.We ran a series of resilient modulus testing according to AASHTO T307 on three types of cohesive soil treated with an enzymatic preparation to investigate its potential on roads construction.The results show significant improvement in the resilient modulus values,estimated at 1.4 to 4.4 times that observed for the untreated soil.Because of the complexity in conducting the resilient modulus measurement,we did a regression analysis to produce reliable correlation formula to predict the resilient modulus for untreated and stabilised soil samples involving stress state.The resilient modulus values for the subgrade materials at the anticipated field stresses were determined using a universal model.The enzymatic preparation was applied in pavement of a sample road and evaluated using the plate load test.SEM analysis for soil samples shows improvement in the soil compaction via reduction of voids between soil particles.XRD analysis shows no major structural changes in the treated soils.The enzymatic preparation contains 43 mg/mL of proteins.We used the SDS-PAGE(sodium dodecyl sulphate polyacrylamide gel electrophoresis)technique to identify the main protein components;however,the presence of interfering materials(surfactants)hinders the separation.

Resilient modulus prediction of soft low-plasticity Piedmont residual soil using dynamic cone penetrometer

Dynamic cone penetrometer（DCP） has been used for decades to estimate the shear strength and stiffness properties of the subgrade soils. There are several empirical correlations in the literature to predict the resilient modulus values at only a specific stress state from DCP data, corresponding to the predefined thicknesses of pavement layers（a 50 mm asphalt wearing course, a 100 mm asphalt binder course and a200 mm aggregate base course）. In this study, field-measured DCP data were utilized to estimate the resilient modulus of low-plasticity subgrade Piedmont residual soil. Piedmont residual soils are in-place weathered soils from igneous and metamorphic rocks, as opposed to transported or compacted soils.Hence the existing empirical correlations might not be applicable for these soils. An experimental program was conducted incorporating field DCP and laboratory resilient modulus tests on "undisturbed" soil specimens. The DCP tests were carried out at various locations in four test sections to evaluate subgrade stiffness variation laterally and with depth. Laboratory resilient modulus test results were analyzed in the context of the mechanistic-empirical pavement design guide（MEPDG） recommended universal constitutive model. A new approach for predicting the resilient modulus from DCP by estimating MEPDG constitutive model coefficients（k;,k;and k;） was developed through statistical analyses. The new model is capable of not only taking into account the in situ soil condition on the basis of field measurements,but also representing the resilient modulus at any stress state which addresses a limitation with existing empirical DCP models and its applicability for a specific case. Validation of the model is demonstrated by using data that were not used for model development, as well as data reported in the literature.