Abstract:To investigate the scaling effect on reinforced concrete （RC） members under blast loads， numerical models were established based on existing explosion test data from RC members with various scaling factors. The research primarily focused on the scaling effect of the concrete material strain rate and demonstrated the feasibility of using scaled finite element models to predict the dynamic response of prototype structures. The results show that if the scaling effect of strain rate is not considered， the smaller the scaling factor， the less the damage and dynamic response of the scaled numerical model match the similarity criteria. For a scaling factor of 0.1， the mid-span displacement errors of RC beams at scaled blast distances of 0.50 m·kg^-1/3 and 0.44 m·kg^-1/3 are 32.5% and 75.1%， respectively. The mid-span displacement errors of RC slabs at scaled distances of 0.591 m·kg^-1/3 and 0.518 m·kg^-1/3 are 45.08% and 46.93%， respectively. After scaling the dynamic increase factor （DIF） curve， the results of numerical model calculations align more closely with similarity ratios， with errors generally less than 10%. Finally， a process was proposed for predicting the dynamic response of prototype structures using scaled test results.

Abstract:To address the issue of excessive floor-mounted equipment acceleration in existing non-isolated indoor substations， a partial isolation retrofit strategy was proposed by introducing isolation bearings between the equipment and the floor slab. Based on this concept， a shaking table test was conducted on a prefabricated steel frame structure representative of an indoor substation. Three isolation schemes were considered： a fixed-base system （FBS）， a base isolation system （BIS）， and a partial isolation system （PIS）. BIS was further categorized into BIS-LNR and BIS-LR， corresponding to the use of linear natural rubber （LNR） bearings and lead rubber （LR） bearings， respectively. Experimental results show that the maximum equipment acceleration in FBS reaches 1.42g， significantly exceeding the allowable limit of 0.6g. In contrast， BIS-LNR， BIS-LR， and PIS reduce the average equipment acceleration by 87%， 69%， and 77%， respectively. In terms of structural response， the maximum inter-story drift of BIS is only 38% of that of FBS， while PIS shows a drift similar to FBS. Both remain well below the code-specified limit of 1/50. These results demonstrate that the proposed PIS retrofit strategy can effectively achieve the dual control of structural and equipment responses in existing indoor substations.

Abstract:Taking the N Pad in the Changning Block as a case study， friction experiments were conducted on shale fault gouges to investigate the frictional behavior of shale faults， followed by quantitative risk assessment （QRA） to evaluate the potential for fault slip. The experimental results indicate that friction coefficients of shale faults range from 0.50 to 0.75. The friction coefficient decreases with the increase of clay content and exhibits a progressive weakening trend with shear displacement. QRA results reveal that under a pore pressure perturbation of 15 MPa induced by hydraulic fracturing， the minimum fault slip probability reaches 64%. The fault slip probability increases significantly with the decrease of friction coefficient and the increase of pore pressure perturbation. To mitigate the risk of casing deformation， hydraulic fracturing operations should avoid faults with clay content exceeding 30% （friction coefficient<0.6） or maintain the injection pressure such that the pore pressure perturbation remains below 10 MPa， thereby reducing the likelihood of fault slippage.

Abstract:To investigate the variation of seismic earth pressure in limited soil under the rigid retaining wall base rotation （RB） mode at non-limit state， a seismic earth pressure calculation formula was derived based on the horizontal differential unit method and the pseudo-dynamic method. The corresponding numerical algorithm was also established. Example analysis shows that the seismic active earth pressure in limited soil exhibits periodic characteristics with time and nonlinear variations with depth， and the calculation results are in good agreement with the results of shaking table tests. Parameter analysis indicates that the seismic earth pressure decreases significantly with the increase of initial internal friction angle， wall base displacement ratio， and the decrease of seismic acceleration coefficient. The total seismic earth pressure is primarily influenced by the initial internal friction angle and wall base displacement ratio， while the seismic acceleration coefficient mainly affects the magnitude of the resultant force. It is shown that the seismic design adopting only the pseudo-static method tends to be unsafe under strong earthquake conditions. This method offers valuable theoretical insights for the seismic design of retaining structures in practical engineering applications.

Abstract:The shear characteristics of bentonite are crucial for maintaining the engineering barrier performance of deep geological repositories. We summarize the response mechanisms of bentonite shear characteristics to the repository’s near-field environment and review recent advancements in shear strength prediction models. Under stress， increases in bentonite shear strength are influenced by a rise in solid phase content and a reduction in pore volume per unit. Under suction conditions， shear behaviors are governed by the lubricating effect of the adsorbed water film and the friction between soil particles. Chemical interactions affect shear behaviors through factors like solution concentration， cation exchange reactions， and mineral phase transitions （under alkaline conditions）. Temperature effects， such as illitization and microstructural rearrangement， cause an irreversible increase in shear strength. Under multi-field coupling conditions， the shear properties are influenced by the intricate interactions among various factors， yet current research in this area remains insufficient. While current prediction models for shear strength under suction are well-developed， chemical interaction models mainly consider solution type and concentration， overlooking the roles of cation exchange and mineral dissolution. Given these limitations， further studies are needed to examine the shear behavior under multi-field coupling， assess the deterioration of shear strength following bentonite self-healing， and enhance the accuracy of prediction models.

Abstract:In order to investigate the impact of interface bonding performance on the non-destructive testing of 3D printed concrete （3DPC）， concrete specimens with different layer counts and time intervals were printed and subjected to non-destructive testing using ultrasonic equipment. Additionally， splitting tests were conducted to determine the interfacial bond strength of 3DPC. It is shown that 3DPC exhibits slower wave propagation speeds and smaller amplitudes compared to cast concrete. Moreover， with the increase in the number of printed layers and time intervals， the amplitude attenuation also increases. Amplitude attenuation can be attributed to three parts： the loss caused by ultrasonic wave transmission between the transducer and the specimen， the along-path loss of concrete height， and the loss caused by the printed interface layer. The ultrasonic wave amplitude of 3DPC is significantly lower compared to cast concrete， as the number of printed layers increases， the interface bond strength of 3DPC initially increases and then decreases， showing a linear relationship with the amplitude loss.

Abstract:Microbially induced calcite precipitation （MICP） is an emerging reinforcement technology known for its high bonding strength and minimal environmental impact. However， its engineering applications still face challenges， such as non-uniform precipitation distribution， high costs， and difficulties in field monitoring. To explore the mechanisms of microbial reinforcement， we investigate the key biochemical reactions and mass transport processes involved in MICP at the pore scale. During MICP， the microbial metabolism produces urease， which catalyzes the hydrolysis of urea， inducing calcite precipitation. The precipitation fills the interparticle voids in porous media， reducing the porosity and permeability， and significantly enhancing the structural strength. Based on microfluidic chip experimental results， we investigate the impact of factors on the precipitation distribution and reinforcement effectiveness， such as bacterial concentration， urease activity， cementation solution concentration， temperature， injection strategies， and soil properties. Furthermore， we review the progress and limitations of pore-scale MICP numerical models， focusing on model completeness and multi-physics coupling. Finally， we discuss potential research directions.

Abstract:Taking the parks along the Liangjiang Greenway in Chongqing as research objects， their carbon sequestration benefits were systematically evaluated and the regulatory mechanisms of community structures were revealed， based on measured data from 36 typical plant community plots and 49 landscaping species. The results show that： at the species scale， 17 high carbon sequestration plants were identified， with the carbon sequestration capacity showing a significant differentiation pattern of trees>shrubs>herbs； at the community scale， cluster analysis revealed that communities with thick trunks， tall stature， and high canopy closure （cluster Ⅰ， cluster Ⅱ） exhibit the optimal carbon sequestration benefits， standing out among 16 high-efficiency communities； regarding the structural regulation， vertical layering should increase the proportion of high carbon sequestration trees while synergistically incorporating high carbon sequestration shrubs and herbs， whereas horizontal pattern follows a gradient efficiency rule of dense forests>moderately dense forests>sparse forests. Accordingly， optimization strategies for high carbon sequestration plant configuration in urban green spaces were proposed from three dimensions： species selection， community construction， and spatial arrangement， providing a theoretical basis and practical pathway for enhancing the carbon sequestration resilience of urban ecosystems and supporting the goal of carbon neutrality.

Abstract:The evaluation of pavement bearing capacity is a crucial aspect in ensuring the safe operation of aircraft. We review the basic principles of the evaluation method of aircraft classification number-pavement classification number （ACN-PCN） and summarize its limitations. The optimization content of the aircraft classification rating-pavement classification rating （ACR-PCR） evaluation method is expounded. The calculation procedures for both ACR and PCR are detailed， and a comparative case study is conducted to analyze the two methods. The results show that the ACR-PCR evaluation method optimizes parameters such as its standard structure and equivalent single tire pressure， and updates the control criteria of the pavement structure failure mode and the calculation method of its mechanical response， thereby achieving alignment with current pavement design methodologies. The study reveals that ACR values cannot be simply represented as ten times ACN values. Moreover， the ACR-PCR evaluation method imposes more stringent requirements on the load-bearing capacity of rigid pavements. It is recommended that on the basis of optimizing the standard structure， the calculation method of ACR for inorganic binder stable base pavement and the ACR-PCR evaluation method for the bearing capacity of composite pavement with overlay on rigid pavement should be proposed．

Abstract:Given the limitations of traditional fixed detection methods in capturing continuous and dynamic vehicle information， we propose an adaptive signal control method （ACV2D method） based on virtual dynamic detection for intersections in a partially connected traffic environment to address the issue of low signal control accuracy. Through the ACV2D method， the position-variable virtual detection section and interval are built to replace conventional traffic flow detectors. After a signal phase gains the right of way， the initial green time is calculated based on the position of the farthest connected vehicle （CV） in the queue. Simultaneously， the measured CV data are used to predict traffic flow conditions within the virtual detection section and interval， as well as the duration of phase green time. During this process， the consistency between the predicted and actual traffic flow conditions within the virtual detection area is monitored. When the prediction results deviate， a real-time correction model for signal control parameters is constructed with the objective of minimizing the average vehicle delay. Taking the predicted vehicle arrival time as the decision point， the dynamic programming method is adopted to solve the optimal signal phase timing in a forward sequence of signal phases. Simulation results demonstrate that when the CV penetration rate exceeds 50%， ACV2D method significantly outperforms reinforcement learning-based adaptive signal control methods， such as 3DQN and 3DRQN， under medium to high traffic volumes. Further research indicates that the effectiveness of the ACV2D method is jointly influenced by two factors， i.e.， CV penetration rate and the sum of key lane group flow ratios Y. The larger the Y value， the lower the required CV penetration rate to ensure the effectiveness of the ACV2D method； conversely， the smaller the Y value， the higher the required CV penetration rate.

Abstract:In this paper， an evaluation stability assessment method based on energy indicators for high-speed maglev vehicle-track systems is proposed. By the establishment of a maglev vehicle-track coupled dynamic model and extensive multi-condition simulations， the systematic mapping between track irregularities and the system’s energy transfer accumulation characteristics is elucidated. And the method’s effectiveness is further validated using measured track irregularity data from the track geometry maintenance of the Shanghai High-speed Maglev Demonstration Line.

Abstract:Based on the correlation between flow structure and thermal environment inside vehicle cabins， five new air supply-out layouts different from traditional layouts were proposed. CFD simulation combined human thermal regulation model was used to analyze the characteristics of the cabin thermal environment under each layout. Then， the equivalent homogeneous temperature （EHT） based thermal comfort model was used to evaluate each layout. It is shown that the mean radiant temperature and mean air velocity have greater impact on the cabin thermal comfort than the average air temperature of the cabin. The top supply-out layout and the two composite layouts demonstrate the best and worst overall performance respectively， while the traditional air supply-out layout displays a high energy consumption with poor comfort uniformity.

Abstract:To explore the method of improving engine efficiency and reducing emissions， the impact of combustion closed-loop control key parameters CA50 on the combustion and emissions of a hydrogen-enriched compressed natural gas （HCNG） engine was experimentally studied， and CA50 based on the experimental results was statistically analyzed. Meanwhile， the particle swarm optimization （PSO） back-propagation neural network （BPNN） algorithm was applied to the prediction of CA50， and the influence of hybrid strategy optimization on the performance of PSO-BPNN model was investigated. Results show that： CA50 has a significant impact on the combustion characteristics and emissions of the HCNG engine； CA50 obeys the normal distribution and has no auto-correlation， so it can be used as the feedback parameter of combustion closed-loop control； the CA50 prediction model established by PSO-BPNN method has the high prediction performance and good generalization ability， with the average absolute error of 0.25°CA and the correlation coefficient of more than 0.997； the hybrid strategy can significantly improve the convergence speed of the model without reducing prediction accuracy， with the CPU running time reduced by up to 73.02%.

Abstract:Aiming at the problem of driver distracted behavior recognition， the information bottleneck theory and the graph convolutional network were combined to realize the action recognition based on the 2D pose estimation， which effectively increases the retention degree of neural network for effective information， so as to make up for the lack of input information. The accurate action recognition was achieved with the limited input information in combination with CTR-GCN.

Abstract:A data asset quality evaluation model considering qualitative and quantitative indexes comprehensively was proposed by combining the cloud model with the best worst method （BWM） and technique for order preference by similarity to an ideal solution （TOPSIS）. Firstly， the quality evaluation index system of data assets was constructed， and the individual natural language evaluation value was transformed into the cloud model based on the golden section method of cloud model. Secondly， the evaluation group decision value was calculated for qualitative and quantitative indexes respectively， and the cloud-TOPSIS decision matrix was obtained by combining the index weights from BWM. Thirdly， the evaluation schemes were ranked qualitatively based on TOPSIS. Finally， the constructed model was applied to the quality evaluation of Alibaba Cloud’s public datasets for the verification of the feasibility and effectiveness of the model.