This paper delves into the lateral load-bearing behavior of lattice-shaped diaphragm wall (LSDW), a novel type of diaphragm wall foundation with many engineering advantages. By employing a double-layer wall structure for the first time in laboratory settings, the research presents an innovative testing methodology, complete with novel computational formulas, to accurately measure the responses of LSDW’s inner and outer walls under varying loads. It is found that the Q–s curves of LSDWs exhibit a continuous, progressive deformation and failure characteristic without any abrupt drops, and the standard for judging the horizontal bearing capacity of LSDW foundations should be based on the allowable displacement of the superstructure. The bearing capacity for the double-chamber LSDWs was found to be approximately 1.68 times that of the single-chamber structure, pointing to a complex interplay between chamber number and structural capacity that extends beyond a linear relationship and incorporates the group wall effect. The study also reveals that LSDWs act as rigid bodies with minimal angular displacement and a consistent tilting deformation, peaking in bending moment at about 0.87 of wall depth from the mud surface, across different chamber configurations. Furthermore, it can be found that using the p–y curve method for analyzing the horizontal behavior of LSDW foundations is feasible, and the hyperbolic p–y curve method offers higher accuracy in calculations. These insights offer valuable guidance for both field and laboratory testing of LSDWs and aid in the design and calculation of foundations under horizontal loads.
In this study, innovative Lightweight Self-compacting Geopolymer concrete made of industrial and agricultural wastes is developed and used as the in-fill material in Fiber Reinforced Polymer (FRP) composite columns. The axial compressive performance of the columns is investigated with critical parameter variations such as the effect of the Diameter to thickness (D/t) ratio and fiber orientation of the FRP tube. Two types of D/t ratios, i.e., 30 and 50, and three fiber orientations ±0°, ±30°, and ±45° were used for the key parameter variations. An increased D/t ratio from 30 to 50 reduces the performance in terms of load despite increasing the deformation. The columns containing the fiber orientation of ±0° exhibit greater performance compared to other types of fiber orientation (±30° and ±45°). The experimental results and failure patterns were compared and validated against the numerical and theoretical studies. A Finite Element model is developed and validated with the experimental results with errors ranging from 0.84% to 4.57%. The experimental results were validated against various existing theoretical prediction models with a percentage error of 7% to 14% An improved theoretical model is proposed for predicting the axial load of concrete-filled FRP composite columns.
In this article, the mechanical properties of tunnel joints with curved bolts are studied and analyzed using the research methods of full-scale testing and finite element numerical simulation. First, the experiment results were analyzed to find out the development law of stress and strain of concrete in each part of the tunnel fragment when bearing. The damage process of the joint of the tunnel fragment was described in stages, and the characteristic load value that can reflect the initial bearing capacity in each stage was proposed. Afterward, using the ABAQUS three-dimensional (3D) finite element numerical modeling software, a numerical model corresponding to the experiment was established. The mid-span deflection was used to observe the change in loading and the destruction of each stage, comparing it to the proposed form to verify the reasonableness of the numerical model. Finally, the numerical models were used to analyze the change in material parameters and external loads from two aspects. It is concluded that the damage process of tunnel joints under curved bolt connection can be divided into concrete elasticity stage, inner arc cracking stage, overall joint damage stage, and ultimate joint damage stage, and the initial load of the adjacent stages is defined as the characteristic load value. After concrete cracking occurs, the bolts start to become the main load-bearing components, and the bolt stress grows rapidly in stage II. The strain development of the concrete on the outer arc is greater than the strain value of the concrete on the side due to mutual contact and extrusion. The parameters were changed for material properties, and it was found that increasing the concrete strength and bolt strength could improve the shield fragment joint bearing performance. The optimal effect of improving the mechanical properties of the shield fragment joint would be obtained when the concrete strength grade is C60, and the bolt strength grade is 8.8. Increasing the size of the axial force and bolt preload has the most obvious effect on the load-carrying capacity in the initial elastic phase. This can reduce the joint angle and thus improve joint stiffness. Meanwhile, increasing the axial force has a greater effect on the performance of the tunnel joint than the bolt preload.
This study develops a machine-based washing and sieving method to accurately determine the soil particle size distribution for classification. This machine-based method is an extension of the recently developed and invented manual-based extended wet sieving method. It revises and upgrades a conventional rotary vibrating sieve machine with a steel sieve of aperture 0.063 mm and ten cloth sieves of apertures from 0.048 to 0.0008 mm for washing and sieving silt and clay. The machine generates three-dimensional motion and vibration, which allows particles smaller than the sieve aperture to pass through the sieve quickly. A common soil in Hong Kong, China, named completely decomposed tuff soil is used as test material for illustration. The silt and clay mixtures are successfully separated into many sub-groups of silt particles and clay particles from 0.063 to less than 0.0008 mm. The test results of the machine-based method are examined in detail and also compared with the manual-based method. The results demonstrate that the machine-based method can shorten the sieving duration and maintain high accuracy. The particle sizes of separated silt and clay particles are further examined with scanning electron microscopic images. The results further demonstrate that the machine-based method can accurately separate the particles of silt and clay with the pre-selected sieve sizes. This paper introduces a new machine-based washing and sieving method, and verifies the efficiency of the machine-based method, the accuracy of particle size, and its applicability to the classification of different types of soil.
The bending capacity of the precast decks is greatly dependent on the flexural strength exhibited by the joints between them. However, due to the complexity and diversity of this system, precise predictive models are currently unavailable. This study introduces an effective and precise methodology for assessing flexural strength using Monte Carlo Model Averaging (MCMA), a statistical technique that combines the strengths of model averaging (MA) and Monte Carlo simulation. To construct the MCMA model, input variables were derived by analyzing the experimental results, and a database of 433 bending test specimens was compiled. The MCMA model incorporated four different machine learning models, namely decision tree (DT), linear regression (LR), adaptive boosting (AdaBoost), and multilayer perceptron (MLP). Comparative analyses revealed that the MCMA model outperformed baseline models (DT, AdaBoost, LR, and MLP) across all employed metrics. The impact of three different categories on flexural capacity was explored through boxplot analysis. Furthermore, a comparison between the MCMA model and the strut and tie model highlighted the superior performance of the MCMA model. The impact of input variables on the flexural strength prediction was further examined through Shapley Additive exPlanations based feature importance and global interpretation, as well as parametric study.
An outliers-free isogeometric modeling method for rotating disk-shaft systems is developed. The Timoshenko beam theory and artificial spring technique are employed for the rotating shaft and elastic boundary conditions. The nonlinear parameterization method is employed for the removal of outliers and three different nonlinear mappings are developed for the discussion of the accuracy of low modes. The energy coupling method between disks and shaft under nonlinear mapping is performed by using the Newton Raphson method. The results show that the isoparametric mapping has better performance in the accuracy of low modes than other nonlinear mapping and the outliers can also be removed, besides, the present method has good convergence rate for different boundary conditions. The accuracy of the proposed method shows good consistency with the Finite Element Method. The time cost of modeling is reduced by 71.4% compared to the traditional rotor model for a multiple disks rotor system, which indicates that the present approach has potential to provide more efficient optimization models of disk-shaft systems. The proposed method can provide a new modeling framework and can be easily extended to the prediction and optimization of vibration characteristics of complex rotor systems with multiple disks and supports.
The employment of large-diameter shield machines has increased the likelihood of encountering composite formations, posing engineering challenges associated with excessive surface settlement. To tackle this issue, this study introduces a hybrid model which integrates the extreme learning machine (ELM) with the sparrow search algorithm (SSA) to predict longitudinal surface settlement. Based on on-site measurements. this study analyzed longitudinal surface settlement patterns across both homogeneous and composite formations. Tunneling parameters, geological parameters, and geometrical parameters were considered as input parameters. Furthermore, this study conducted a comparative analysis of the predictive performance among SSA-ELM, ELM, and SSA-back propagation (BP), with respect to coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and training time. Last, in anticipation of potential risks, a feasible optimization approach is provided. SSA-ELM outperforms both ELM and SSA-BP in terms of R2, MAE, and RMSE, with values of 0.8822, 0.3357, and 0.4072, respectively. Regarding training time, SSA-ELM requires 0.2346 s, prior to SSA-BP with a value of 1.8427. Although it is not as fast as ELM, the discrepancy between SSA-ELM and ELM is only 0.1187 s. Overall, SSA-ELM demonstrates higher performance and serves as an effective tool to guide the construction process.
The steel–concrete composite bridge system with twin girders, referred to as a steel plate composite girder bridge, is widely adopted for short- to medium-span highway bridges due to its ability to enable rapid prefabrication and construction in bridge engineering. Considering the structural design of steel plate composite girder bridges, which are wide but shallow in depth, their deck slabs are vulnerable to vertical impacts from vehicle loads. Structural performance may be negatively affected by excessive dynamic displacement of deck slabs. It is difficult to assess the dynamic response of the deck slabs by existing methods, since traditional specifications only use a global impact factor to describe the dynamic effect of moving vehicles on the bridge as a whole, regardless of the local dynamic effect on the deck slabs. Therefore, this study aims to assess the local dynamic effect of moving vehicles on the deck slabs of steel plate composite beam bridges using field tests and finite-element methods. A systematic approach was employed to analyze parameters influencing bridge-vehicle interaction. Additionally, an improved method was presented to calculate the local impact factor and parametric studies were discussed. The findings indicated that the local impact factor of deck slabs is significantly greater than the global impact factor. Road surface roughness is the most significant parameter affecting deck slab dynamic behavior.
This work utilizes the finite element approach together with an innovative shear strain theory to investigate the static bending behavior, free vibration features, and static buckling phenomena of flexo-magnetic nanoplates. The inquiry specifically examines the fluctuation in both the thickness of the plate and the elasticity of the foundation. The influence of initial geometrical imperfections, including several categories such as local and global faults, is also taken into account. The influences of several factors, including the law governing thickness fluctuation, types of imperfections, boundary conditions, and elastic foundation, on the mechanical response of the plate are considered. Outcomes of the work include new and original discoveries that have not been discussed in previous research, adding to both theoretical comprehension and practical implementation.
Machine learning methods have advantages in predicting excavation-induced lateral wall displacements. Due to lack of sufficient field data, training data for prediction models were often derived from the results of numerical simulations, leading to poor prediction accuracy. Based on a specific quantity of data, a multivariate adaptive regression splines method (MARS) was introduced to predict lateral wall deflections caused by deep excavations in thick water-rich sands. Sensitivity of lateral wall deflections to affecting factors was analyzed. It is disclosed that dewatering mode has the most significant influence on lateral wall deflections, while the soil cohesion has the least influence. Using cross-validation analysis, weights were introduced to modify the MARS method to optimize the prediction model. Comparison of the predicted and measured deflections shows that the prediction based on the modified multivariate adaptive regression splines method (MMARS) is more accurate than that based on the traditional MARS method. The prediction model established in this paper can help engineers make predictions for wall displacement, and the proposed methodology can also serve as a reference for researchers to develop prediction models.
