In this paper, the two-scale random field model is introduced within the framework of the elastic-plastic stochastic damage mechanics for representing the spatial variation of concrete. A framework for stochastic nonlinear response analysis is developed, where the probability density evolution method is adopted to quantify the propagation of randomness in concrete structures, and the two-scale random field model is applied to represent the spatial variation of concrete. The proposed framework is demonstrated by an illustrative example, which address the stochastic nonlinear response of the reinforced concrete frame structure.

Concrete materials contain intrinsic randomness in their mechanical properties, and this inherent uncertainty shows statistically significant differences under various loading cases. This paper reveals the propagation behavior of concrete randomness under varying loads by combining the relationship between static strength, dynamic strength, and fatigue life from the perspectives of statistical moments and probability density. The findings indicate that the standard deviation of concrete's dynamic strength rises as strain rate increases. The nonlinear relationship between fatigue stress and fatigue life (SN curves) accounts for fatigue life's notable discreteness. First- and second-order statistical moments can accurately reflect the randomness propagation behavior when the relationship between random variables is linear or nearly linear. However, when the relationship between random variables is highly nonlinear, the probability density approach must be adopted.

This study investigates the coupling risk factors between urban water supply and transportation networks by analyzing 246 pipe damage incidents (with diameters ≥800 mm) in the central area of Puxi, Shanghai, from 2004 to 2021. It employs an N (network scale) -K (interaction degree) model to construct a risk coupling model for the two networks, revealing the multi-factor risk cascading effects and coupling characteristics of the water supply-transportation coupled network. The findings demonstrate that there exists risk factor coupling between the two networks, with increased coupling corresponding to greater safety risk impacts. The physical attributes of the water supply network itself constitute the primary risk factor affecting its safety. Based on the system decoupling theory, this paper proposes risk control strategies for urban water supply-transportation network coupling, which are of great significance for reducing coupling interactions between the two networks and ensuring their safe operation.

The connotation of shield tunnel structure resilience was discussed in this paper, based on the adaptive cycling theory and characteristics of structural entire disaster process. Additionally, structural performance evaluation indexes and analysis model were proposed according to structural performance allowances. Moreover resilience grading criteria based on structural performance, restoration cost and affected time were presented. Furthermore, a comprehensive resilience evaluation method for shield tunnel structures of the entire disaster process was proposed, using the analytic hierarchy process and expert confidence index method to establish a comprehensive resilience index evaluation system and determine the weights of indexes. Finally, the reasonableness of this strategy was demonstrated using an engineering field case.

For long span double-deck suspension bridges with steel truss girder, a new aeroelastic model design method is proposed, referred to as the multi-steel beam frame system (MSBFS), which comprises upper and lower steel beams, horizontal connecting rigid beams, and vertical rigid columns. By transforming the design process into an optimization problem and employing the constrained pattern search method, the design variables for the stiffening beam satisfying the design criteria are obtained. The sensitivity analysis reveals that the modal frequencies are significantly influenced by the dimensions of the horizontal connecting rigid beams, as well as the upper and lower longitudinal steel beams. The dynamic characteristic test results confirm the accuracy of the MSBFS, as the deviation in low-order modal frequencies from the design target values falls within ±5%. As a valuable complement to the full-bridge aeroelastic model design approaches, this design method is well-suited for truss girders, enabling an overall optimal simulation of aerodynamic shape, stiffness and mass system.

An experimental study was conducted on reinforced concrete (RC) columns strengthened using three methods, carbon fiber reinforced polymer (CFRP) jacketing, textile reinforced mortar (TRM) jacketing, and textile reinforced ultra-high toughness cementitious composite (UHTCC) jacketing, under horizontal cyclic loading at different shear span ratios. The study investigated the failure mechanisms and seismic strengthening effects of these strengthening methods. The experimental results reveal that TRU- and TRM- jacketed columns show vertical main cracks at the corners and bending main cracks at the column base, with the TRU jacketing demonstrating better overall integrity than the TRM jacketing. At lower shear span ratios, diagonal cracks in TRU jacketing were significantly less severe than those in TRM ones. In contrast, the CFRP jacketed columns show bending cracks in the plastic hinge region when the shear span ratio exceeded 2.58. While all three strengthening methods provided only modest improvements, they significantly enhanced horizontal deformation capacity and ductility, by up to 100%, and improved energy dissipation capacity by 1.7 to 4.0 times. Among the three strengthening methods, the enhancement in deformation and energy dissipation capacities follow the order of CFRP>TRU> TRM.

Based on the existing concept and architecture of the smart runway, this paper addresses the challenge of managing the smart runway data flow. A systematic analysis of the data characteristics reveals that the data are massive, multi-source, heterogeneous, and multi-frequency. Based on this analysis, it proposes a multi-level data integration framework, a multi-platform physical structure, and a comprehensive data flow architecture for the smart runway. To support efficient and reliable data utilization, as well as classified and long-term data preservation, it develops detailed methods for data preprocessing and standardization. Additionally, it examines the characteristics of smart runway data storage, leading to the design of a corresponding multi-level storage architecture and appropriate storage strategies. The implementation of the proposed system at Rikaze Dingri Airport shows a reduction of over 95% in invalid data without any loss of effective information, achieving unified management and multi-source data sharing. This paper supports the enhanced capabilities of the smart runway, including real-time performance evaluation, advanced risk warning, and intelligent maintenance decision-making.

Based on the vehicle trajectory data of urban underground roads in Shanghai, this paper proposes a theory-data driven vehicle car-following model in urban underground roads. The vehicle trajectory data are collected and processed by millimeter wave radar to screen the car-following behavior. Based on the characteristics of car-following behavior on underground roads, a theory-driven intelligent driver model (IDM) and a data-driven long short-term memory (LSTM) car-following model are developed for acceleration, velocity, and distance headway, and error comparison is conducted. Finally, the prediction results of the two types of models are regarded as two sets of observations for the same system state obtained by different observation methods, and the real driving state is predicted by the adaptive Kalman filter method. The IDM-LSTM hybrid model based on speed prediction is developed, achieving a root mean square error of 0.118 7, outperforms the single IDM (0.583 6) and LSTM (0.123 9) models. This paper can provide valuable reference for the operation and road safety management of urban underground roads.

The mismatch between supply and demand in public transport resource allocation leads to inefficiencies in operations and waste of resources. Therefore, it is essential to identify mismatched regions in public transport resource allocation. However, due to the scale effect of public transport resource allocation and the differences in mobility and travel preferences among regions, it is challenging to identify the mismatch relationship. By calculating the locational quotient (LQ) to identify regions of imbalance between supply and demand, and using the multi-scale geographically weighted regression (MGWR) model to analyze the supply and demand adjustment mechanism with spatial differentiation, this paper comprehensively identified the mismatched regions of public transport resource, and conducted an empirical analysis in Jiading District, Shanghai. The regression results show that the residential-job density, bus service frequency, factory land ratio, and metro station presence have significant local effects on ridership, indicating strong spatial heterogeneity. Based on the induced ridership potential of residential-job density and the bus service frequency, this paper classified the region into four categories: “strong-strong”, “strong-weak”, “weak-strong” and “weak-weak”. Then it proposed differentiated strategies for public transport development. The “strong-strong” and “weak-weak” types represent regions with mismatched supply and demand, where it is recommended to adjust public transport supply using LQ analysis. For the “strong-weak” and “weak-strong” types, strategies should focus on optimization of the network layout and introduction of travel demand, respectively. The proposed method for identifying mismatched public transport resource regions contributes to the theoretical advancement of scientifically allocating public transport resources and improving efficiency while reducing costs.

Based on nearly eight years of data collected from Weibo, eight topics related to chain rear-end collisions were identified using the latent Dirichlet allocation (LDA) topic model and social network analysis, revealing the main characteristics and mechanisms of these accidents. The study finds that highways are the primary accident scenes. Accident frequency during rain and snow is higher than in foggy conditions. Failure to maintain a safe following distance and speeding significantly increase accident risk. By assigning topics to each document using LDA and extracting severity information through regular expressions, an ordered logit regression model was constructed to analyze the impact of different topics on accident severity. The results show that the average number of injuries and fatalities in chain rear-end collisions are 2.12 and 1.85 times higher the averages for general motor vehicle traffic accidents, respectively. The severity of chain rear-end collisions on highways is higher than at intersections, with an odds ratio (OR) 3.3 times that of intersections. The OR for foggy-related accidents is 9.4 times that of rainy and snow, while the OR for accidents involving trucks is 4.6 times that of cars is 2.2 times that of buses.

In order to reasonably assess the risk of contaminated sites, the total amount of Cu, Pb, Cd, and As heavy metals in the soil was determined by taking the farmland soil downstream of a copper pile leaching site in Yunnan, China as an example. The occurrence forms of the four heavy metals were determined by improved BCR (Bureau of European Communities Reference) continuous extraction method, and the correlation among the four heavy metals was analyzed. The soil heavy metal pollution and potential risk were evaluated by in-situ accumulation index (Igeo), primary and secondary comparison value (RSP) and the risk assessment coding method (RAC). The results show that the average contents of Cu, Pb, Cd, and As are 39.50, 46.98, 0.16, and 82.67 mg·kg-1. Compared with the background value of heavy metals in Yunnan, the over-standard rates of the four heavy metals are 0%, 83.33%, 33.33%, and 100%, among which the pollution of As required special attention. The results of heavy metal occurrence indicate that Cu, Pb, Cd, and As are mainly in residual form, accounting for 83.14% to 95.62%, suggesting that the four heavy metals exist in stable form in soil. A comparison of the four evaluation methods demonstrates that there are significant differences in evaluation results. The Igeo results show that only As pollution exceeds the standard, while the other three heavy metals are pollution-free. The RSP results show that the four heavy metals are pollution-free to the environment. The RAC results show that Cd has a low risk to the soil environment, while the other three heavy metals had no risk. By comparison, it is found that only the total amount of heavy metals in the soil of the polluted site as an evaluation index would exaggerate the pollution risk to a certain extent, and the analysis should be conducted by combining the total amount of heavy metals and the content of their occurrence forms. Although the total concentration of heavy metal pollutants is relatively high, the content of a bioavailable heavy metal is very low, resulting in a minimal risk of diffusion into the environment. Therefore, the study area is subject to mild heavy metal contamination, with a low level of potential ecological risk.

The influence of complexing agents including formic acid, oxalic acid and phosphoric acid on the storage stability of aluminum sulfate-based alkali-free liquid accelerator (LA), the setting time of cement paste and the compressive strength of mortar were studied in the present research. And their mechanism were investigated by analyzing hydration process as well as the phase composition and microscopic morphology of cement paste containing aluminum sulfate-based LA using hydration heat flow calorimetry, X-ray diffraction (XRD), thermogravimetric-derivative thermogravimetric (TG-DTG) analysis and scanning electron microscope (SEM). The results show that three kinds of complexing agents improved the storage stability of aluminum sulfate-based LA, and their effect was in the order of phosphoric acid > formic acid > oxalic acid. At the same aluminum sulfate proportion, with the introduction of three individual complexing agent into aluminum sulfate-based LA, the setting time of cement paste was shortened significantly. Among three complexing agents, their effect on improving the accelerating effect of aluminum sulfate-based LA was in the order of formic acid > oxalic acid > phosphoric acid. Using oxalic acid as complexing agent improved the early strength effect of aluminum sulfate-based LA, while phosphoric acid was beneficial to improving the 28 d compressive strength of mortar. The acid radical ions of complexing agents can form a stable complex with Al3+ in aluminum sulfate-based LA, which can increase the content of Al3+ and the amount of AFt formed in the hydrated system, thus enhance the accelerating and early strength effect of aluminum sulfate-based LA.

The basic theoretical models established by domestic and foreign researches on hydrogen leakage are reviewed. The characteristics of hydrogen leakage are described from jet structure, diffusion plume, concentration accumulation, etc., and the latest research results on hydrogen leakage based on numerical simulation are introduced. The buoyancy jet models under slow hydrogen leakage are developed. However, model prediction under under-expanded hydrogen jet conditions presents great challenges. The numerical simulation method is very suitable for studying the diffusion law of hydrogen leakage in complex scenarios, but more experimental data are needed. The hydrogen prediction models based on advanced machine learning technology show great application prospect. In the future, hydrogen leakage models need to be further developed to broaden the applicable scenarios and improve the prediction accuracy.

To monitor and improve the geolocation accuracy of domestic Synthetic Aperture Radar (SAR) payloads, this paper identifies wind turbine locations across three terrain types using high-resolution remote sensing imagery and deep learning models. Leveraging the strong scattering characteristics of wind turbines in SAR imagery, it constructs a large-scale ground control point (GCP) database for long-term and wide-area geometric processing of SAR images. The results show that Gaofen-3 Fine Strip II (FSII) mode imagery exhibits periodic fluctuations in geolocation accuracy from 2017 to 2020, with a cycle of approximately 747.99 days, during which the accuracy gradually degrades from its peak. It also analyzes the influence of terrain on geometric positioning, revealing that mountainous areas experience the lowest positioning accuracy. Using the constructed GCP database, the average geolocation error of Gaofen-3 FSII imagery is reduced from 44.64 meters to 7.94 meters across various terrains, achieving consistent accuracy across all three terrain types.

Understanding the hydrocarbon charge history and accumulation process is crucial to petroleum exploration in the Bongor Basin, Chad. The chronology of hydrocarbon accumulations in the buried-hill of the basin was investigated by using a suite of techniques including organic geochemistry, fluid inclusion, Fourier transform infrared (FT-IR) spectroscopy, apatite fission track analysis (AFTA) and thermal history modeling. The results indicate that in the Bongor Basin, Subzone B and Subzone R-P of the buried-hill belt contain two types of oil inclusions exhibiting yellow-green and blue-green fluorescence, representing different stages of hydrocarbon charging. Based on AFTA and tuff zircon fission track data, it is inferred that different buried-hill zones experienced two phases of primary hydrocarbon accumulation, both peaking during 75-65 million years. In Subzone B, a secondary hydrocarbon accumulation event occurred during the uplift and reworking stage since the Late Cretaceous, characterized by hydrocarbon adjustment and redistribution. Most hydrocarbons are hosted in structural fractures, with the adjustment phase occurring approximately 30 million years ago.