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In the realm of metal mine tailings management, enhancing the performance of Cemented Lithium Feldspar Tailings Backfill Composites (CTBC) is highly crucial. This study delved into the influence of fiber on the flexural strength of CTBC. Employing orthogonal three-point bending tests and numerical simulations, it examined how fiber type, content, solid content, and the cement-to-tailings ratio (c/t) affected CTBC flexural strength and post-peak toughness. Findings demonstrated that fiber content and c/t notably influenced flexural strength, with c/t being more significant (weights of 1.78 and 7.04 respectively via range). The flexural strength of CTBC changed with fiber content and c/t, and 70 % was the optimal solid content. PVA fibers exhibited the best enhancement. The yield deflection of fiber-reinforced CTBC exceeded that at the peak, and post-peak toughness was mainly affected by fiber content and type (relative values 2.25 and 3.625). CTBC beams cracked prior to peak load, and fiber addition postponed crack extension. The fitting model boasted high prediction reliability (correlation coefficients for flexural strength, deflection, and peak load regression models were 1, 0.988, and 0.999 respectively; correction coefficients were 1, 0.987, and 0.999 respectively)

In response to the problem that existing impact hazard assessment methods cannot accurately reflect the features of stress concentration changes under the influence of impact hazard factors, an impact hazard area classification method base on multi factor coupled quantitative characterization model is proposed. Firstly, based on the geological conditions of the underground coal seam, the distribution of roadways, and the scope of mining, the main impact influencing factors are analyzed. Secondly, referring to the multi factor superposition method and stress analysis method respectively, the method determines the influence range and relative stress concentration coefficient of various impact influencing factors. Thirdly, based on the distribution function of micro element strength inside the coal rock mass, a multi factor coupled quantitative characterization model for impact hazard is constructed. Finally, the impact range and relative stress concentration coefficient of the impact influencing factors are input into the quantitative characterization model to obtain the stress distribution results of the coal seam. Based on the stress distribution results, the impact hazard level is classified, and the distribution of impact hazard areas is obtained. Taking the No.3 coal seam of Shandong Xinjulong Energy Co., Ltd. as an example, by analyzing the stress concentration caused by the main impact risk factors such as superimposed faults, large roadways, and goaf, a reasonable impact hazard level classification standard is formulated. The results of the impact hazard area classification of the No.3 coal seam are obtained and verified on site. According to the mining seismic events that occurred before and after the completion of the evaluation work, it can be seen that the impact source is mainly concentrated in the strong impact hazard area. It is consistent with the regional division results, thus verifying that this method can effectively quantitatively divide the coal seam impact hazard area.

Abstract In this study, we propose a piezoelectric ceramic sensor-based health monitoring method to monitor the stress state of diagonally braced H-shaped steel structures. Loading experiments were carried out on diagonally braced H-shaped steel under different working conditions. Subsequently, the amplitude and energy of piezoelectric signals under these two working conditions were compared and analyzed, and finite element analysis was performed using ABAQUS software to verify the results. The experimental results showed that with an increase in the web height or load, the time-domain waveform energy index of the H-shaped steel increased. Under different working conditions, such as a diagonally braced H-shaped steel member with a web height of 10 cm, when the pressure value was less than 10 N/mm2, the energy index increased by approximately 15.98% for every 1 N/mm2 increase in the pressure. When the pressure value was greater than 10 N/mm2 and less than 15 N/mm2, the energy index increased by approximately 1% for every 1 N/mm2 increase in pressure. Further, the energy index increased by approximately 8.4% for every 1 cm increase in the height of the web. Simultaneously, it can be seen from the results of the finite element analysis that the stress and strain at the induction position of the piezoelectric ceramic sensor increased with an increase in the external pressure. The study of structural health monitoring for diagonally braced H-shaped steel structures holds significant importance in ensuring the safety and reliability of these structures, achieving predictive maintenance, evaluating structural performance and energy efficiency, and optimizing structural maintenance.

Urbanization and population concentration in China’s major cities drive high land utilization demands, affecting nearby bridges during underground construction. Foundation pit construction alters the internal forces, deformation, and displacement of bridge piles. To understand these impacts and assess excavation support rationality, a case study was conducted on an ultra-deep foundation pit near an elevated ring road bridge in Wuhan. Considering the engineering geological conditions of the project site, construction sequence, and traffic load on the bridge, a three-dimensional finite element model was established to simulate the impact of foundation pit excavation on the elevated structure. And through an analysis of tracked monitoring data from the construction site, comparisons were also made with the simulation results. The findings of this case study indicated that throughout the entire construction phase of the foundation pit, the maximum horizontal and vertical displacements of the bridge foundation structure caused by the construction are 2.98 mm and −1.75 mm, respectively; the maximum change rate of the bending moment in the bridge structure due to the foundation pit construction is 6.3%, while the change in the axial forces is small, and completely within the safety control standards for bridge structure displacement. By analyzing the monitoring data over three stages within one year after the completion of the foundation pit, it is shown that the bridge structure gradually tends to stabilize. Additionally, due to soil consolidation, its displacement shows uniform rebound and tends to stabilize. The research findings provide valuable reference points for the design and construction of similar deep foundation pit projects.

Excavation in foundation pits can result in serious issues for nearby tunnel structures like deformation, differential settlement, and seepage damage, which profoundly impact project timelines and potentially endanger life and property safety. Therefore, it is imperative to investigate these impacts before and after construction and to facilitate timely adjustments of construction measures and reinforcement where possible. In this study, a foundation pit construction project near a rail transit line is employed as a case to comprehensive study the impact of on-site deep foundation pit excavation on adjacent rail transit structures by numerical simulation. A three-dimensional finite-element model of the foundation pit based on site geological characteristics and construction procedures is established to study the excavation and maintenance processes. Through analysis of key parameters including soil deformation, displacement, shear force, and bending moment of the tunnel structures, the designed protective structure is found to have effectively mitigated soil deformation, ensuring the stability of the foundation pit. As excavation progresses, lateral soil deformation and vertical uplift gradually increase but remain within specified control values. During various excavation stages, the maximum displacement of the tunnel structure gradually increases, with the increase rates of maximum settlement being 29.09%, 20.51%, and 6.45%, respectively. This indicates a gradual enhancement of the stability of the tunnel structure. Additionally, excavation of the foundation pit has a significant impact on the bending moment distribution of the tunnel structure but does not affect the axial force and shear force of the tunnel structure. The findings of this study offer crucial scientific insights for evaluating the safety and stability of construction near tunnel structures.

Our purpose was to devise a radiomics model using preoperative computed tomography angiography (CTA) images to differentiate new from old emboli of acute lower limb arterial embolism. 57 patients (95 regions of interest; training set: n = 57; internal validation set: n = 38) with femoral popliteal acute lower limb arterial embolism confirmed by pathology and with preoperative CTA images were retrospectively analyzed. We selected the best prediction model according to the model performance tested by area under the curve (AUC) analysis across 1,000 iterations of prediction from three most common machine learning methods: support vector machine, feed-forward neural network (FNN), and random forest, through several steps of feature selection. Then, the selected best model was also validated in an external validation dataset (n = 24). The established radiomics signature had good predictive efficacy. FNN exhibited the best model performance on the training and validation groups: its AUC value was 0.960 (95% CI, 0.899–1). The accuracy of this model was 89.5%, and its sensitivity and specificity were 0.938 and 0.864, respectively. The AUC of external validation dataset was 0.793. Our radiomics model based on preoperative CTA images is valuable. The radiomics approach of preoperative CTA to differentiate new emboli from old is feasible.

This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street metrics and GIS, the study analysed the urban form through the random forest method, which reveals the predictive features of urban patterns and enables a deeper understanding of the spatial structures of cities. The findings showed distinctive spatial structures, such as ring formations and urban cores, indicating stages of urban development and socioeconomic narratives. It also showed that the unit of analysis has a major impact on the identification and study of street patterns. Concluding that machine learning is a critical tool in urban morphology, the research suggests that future studies should expand this framework to include more cities and urban elements. This would enhance the predictive modelling of urban growth and inform sustainable, human-centric urban planning. The implications of this study are significant for policymakers and urban planners seeking to harness data-driven insights for the development of cities.

In this paper, a novel solar driven smoke ventilation system was proposed for the moxibustion therapy. A mathematical model is developed based on heat balance and fluid flow, and the performance of the ventilation system is studied. Sensitivity analyses of solar radiation intensity from 100 to 1000 W/m2, collector length from 1 to 10 m, solar system width from 0.5 to 3 m, and chimney width from 0.1 to 0.3 m, and chimney height from 0.5 to 3 m are also performed. For stronger solar radiation, larger collector or higher chimney, the effective heat flux of the solar system increases and the flow rate increases. For the solar radiation of larger than 210 W/m2, the velocity at the smoke pipe entrance is larger than the critical velocity of 0.5 m/s. Higher chimney also leads to the increase of the driving force besides receiving more solar radiation. For reaching the pipe entrance velocity of larger than the critical velocity, a solar radiation of larger than 210 W/m2, a chimney height of higher than 0.89 m, a collector length of larger than 1.9 m, or a collector width of larger than 0.198 m is needed. The conclusions are obtained under the absent parametric values: the solar radiation intensity of 600 W/m2, the chimney height of 2 m, the solar collector length of 8 m, and the widths of solar collector and chimney of 0.25 m. This work lays a solid foundation for application of this solar driven smoke ventilation system to the moxibustion therapy.

The bonding state of the pile-soil interaction is complex. Traditional monitoring methods and tools have not been fully applied to monitor and evaluate it although it affects the lifecycle safety of the pile. In this study, a health monitoring method is proposed to evaluate the bonding state of the pile-soil contact area; it is a transient impact response method based on piezoelectric ceramic sensors to monitor the pile-soil bonding state. During the test, different damage degrees of the pile-soil bonding state were simulated by considering the working conditions of different soil densities and different crack depths as examples. A horizontal transient impact stress was applied to the pile top, and a piezoelectric ceramic sensor embedded in the pile detected the stress wave. As the stress wave response differs in different damage conditions, an energy index was established to quantitatively monitor the degree of damage.

The conventional methods used to analyze the shear lag effect in simply supported box girders assume that the neutral axis of the section coincides with the centroid, which does not strictly satisfy the axial equilibrium condition. To address this problem, this study proposes an analysis method in which three independent functions for the shear lag are employed to define the different shear lag strengths of the top slab, the bottom slab, and the cantilever slab. To fulfill the axial equilibrium condition of the box girder and to automatically locate the neutral axis position, the longitudinal displacement of the web is introduced. The shear deformation of the box girder is also considered. The governing differential equations and corresponding boundary conditions for displacement variables such as deflection and rotation of the box girder are derived through the application of the principle of virtual work. The differential equations are solved by utilizing the boundary conditions to obtain the analytical expressions of the shear lag function, longitudinal displacement of the web, rotation, deflection, and neutral axis position. Furthermore, after performing the finite element analysis, the effectiveness of the proposed method is verified by comparing the results with those obtained from conventional methods and finite element analysis. Furthermore, the influence of the axial equilibrium condition is quantified on axial force and stress difference ratios under three methods. Finally, extensive parametric analysis is carried out to investigate the effect of different parameter ratios on the ratios of the stress difference of the flanges. The results show that when the axial equilibrium condition is not considered, the axial stresses in the upper flange of the simply supported box girder are underestimated, especially at the intersection of the top, cantilever slab, and web, and the axial stresses in the lower flange are overestimated. As a result, the method in this study is able to calculate the axial stresses and deflections on simply supported box girders more accurately.

Damage identification plays an important role in enhancing resilience by facilitating precise detection and assessment of structural impairments, thereby strengthening the resilience of critical infrastructure. A current challenge of vibration-based damage detection methods is the difficulty of enhancing the precision of the detection results. This problem can be approached through improving the noise reduction performance of algorithms. A novel method based partially on the errors-in-variables (EIV) model and its total least-squares (LS) algorithm is proposed in this study. Compared with a classical damage detection approach involving adoption of auto-regressive (AR) models and the least-squares (LS) method, the proposed method accounts for all the observation errors as well as the relationships between them, especially in an elevated level of noise, which leads to a better accuracy. Accordingly, a shaking table test and its corresponding finite element simulation of a full-scale web steel structure were conducted. The acceleration time-series output data of the model after suffering from different seismic intensities were used to identify damage using the presented detection method. The response and identification results of the experiment and the finite element analysis are consistent. The finding of this paper indicated that the presented approach is capable of detecting damage with a higher accuracy, especially when the signal noise is high.

Abstract Background Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model. Results This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911. Conclusion In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model’s predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.

The influence of various seismic parameters on the seismic performance of rectangular reinforced recycled concrete columns was comprehensively revealed through collecting and screening experimental data from 53 rectangular recycled aggregate concrete columns. The research results showed that the bearing capacity of the specimens decreased and the ductility performance increased, which were attributed to the following factors. a. The increase in the replacement ratio and slenderness ratio of recycled coarse aggregate, and b. the reduction in the axial compression ratio and the strength of recycled aggregate concrete. The seismic design limitations and boundary conditions of this study are γ ∈ [0, 100%], n0 ∈ [0, 0.9], λ0 ∈ [5.06, 18.479], λ ∈ [1.09, 3.93], ρsl ∈ [1.01%, 2.45%], ρsv ∈ [1.4%, 5.33%], and f ∈ [C30, C50]. Thus, it is recommended that for the seismic design value of rectangular recycled aggregate concrete columns, the optimal longitudinal reinforcement rate be set at 1.59%.

Aiming at some stress-induced failure phenomena in surrounding rock that occur during the construction of super-long and deep-buried tunnels, a method for estimating the in situ stress in the tunnels based on multivariate information integration is proposed, which uses a small amount of in situ stress measurement, stereographic projection technology, and a numerical simulation method. Firstly, by conducting a macroscopic analysis of the regional geological structure, topography, and pre-excavated small tunnels (such as exploration of adits and pilot tunnels), the strength of the tectonic stress field and the orientation of the principal stresses in the tunnel sections are preliminarily determined. Secondly, the reliability of the in situ stress measurement data were analyzed using full-space stereographic projection and the plane stress projection method. Then, some representative measurement points that reflected the distribution characteristics of in situ stress in the project area, on the whole, were determined. Thirdly, the finite difference (FDM) and multiple regression analysis (MRA) methods were used to inverse the in situ stress field in the project area. The proposed method was applied to a super-long and deep-buried tunnel project in Qinling, and the in situ stress distribution characteristics of the tunnel sections at different mileages were obtained. The results show that both the calculated principal stress values and the azimuth angle of the maximum horizontal principal stress are in good agreement with the measured ones, indicating that the method used in this study is reasonable. Finally, the typical surrounding rock failure phenomena encountered during the excavation of the project were investigated, and targeted treatment measures were proposed. The research results can provide references for support design and disaster management of surrounding rock in deep-buried long tunnels.

The automatic identification of urban functional regions (UFRs) is crucial for urban planning and management. A key issue involved in URF classification is to properly determine the basic functional units, for which popular practices are usually based upon existing land use boundaries or road networks. Such practices suffer from the unavailability of existing datasets, leading to difficulty in large-scale mapping. To deal with this problem, this paper presents a method to automatically obtain functional units for URF classification using high-resolution remote sensing images. We develop a context-aware segmentation network to simultaneously extract buildings and road networks from remote sensing images. The extracted road networks are used for partitioning functional units, upon which five main building types are distinguished considering building height, morphology, and geometry. Finally, the UFRs are classified according to the distribution of building types. We conducted experiments using a GaoFen-2 satellite image with a spatial resolution of 0.8 m acquired in Fuzhou, China. Experimental results showed that the proposed segmentation network performed better than other convolutional neural network segmentation methods (i.e., PSPNet, Deeplabv3+, DANet, and JointNet), with an increase of F1-score up to 1.37% and 1.19% for road and building extraction, respectively. Results also showed that the residential regions, accounting for most of the urban areas, identified by the proposed method had a user accuracy of 94%, implying the promise of the proposed method for deriving the spatial units and the types of urban functional regions.

Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks.

A building developed by Wuhan Shimao Group in Wuhan, China, is a high-rise residence with 56 stories near the Yangtze River. The building is a reinforced concrete structure, featuring with a nonregular T-type plane and a height 179.6 m, which is out of the restrictions specified by the China Technical Specification for Concrete Structures of Tall Building (JGJ3-2010). To investigate its seismic performance, a shaking table test with a 1/30 scale model is carried out in Structural Laboratory in Wuhan University of Technology. The dynamic characteristics and the responses of the model subject to different seismic intensities are investigated via the analyzing of shaking table test data and the observed cracking pattern of the scaled model. Finite element analysis of the shaking table model is also established, and the results are coincident well with the test. An autoregressive method is also presented to identify the damage of the structure after suffering from different waves, and the results coincide well with the test and numerical simulation. The shaking table model test, numerical analysis, and damage identification prove that this building is well designed and can be safely put into use. Suggestions and measures to improve the seismic performance of structures are also presented.

Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLSE) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLSE method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure.