This paper proposes a carbon fiber reinforced polymer (CFRP) retrofitting scheme for improving the seismic performance of atrium-style metro stations (AMS). Past experimental studies have confirmed that the weakest of the AMS during strong earthquakes is located at the upper-story beam ends. However, there is thus far no candidate for a reference approach to retrofitting and strengthening the AMS. This study addresses this gap by applying CFRP retrofitting to both ends of the upper-story beam. The main objective is to assess the effectiveness of the proposed retrofitting scheme. First, a three-dimensional finite element model is developed to simulate dynamic soil-AMS interaction. The validity of the numerical method is assessed via a comparison with measured data from reduced-scale model tests. Second, a numerical model of the AMS retrofitted with CFRP is built using validated methods. Finally, dynamic time-history analyses of the AMS with and without CFRP retrofitting are conducted, and their dynamic responses, including inter-story drift, dynamic strain, and tensile damage, in conjunction with the lateral displacement of the surrounding ground, are compared. Comparison of the results for the non-retrofitted and retrofitted structures shows that CFRP retrofitting significantly reduces both the principal strains and tensile damage factors at the upper-story beam ends while slightly increasing those values at the mid-span of the beam; additionally, it does not change the structural lateral deformation. Therefore, it can be concluded that CFRP retrofitting could effectively improve the seismic performance of the AMS without changing its lateral stiffness.

With the widespread application of deep excavation projects, deformation control of diaphragm walls and management of surrounding soil displacement have become major challenges in the engineering field. To address these issues, this study proposes a prefabricated multi-limb composite concrete-filled steel tube (CFST) internal support system. The mechanical performance and deformation characteristics of the fixed ends of the system were systematically analyzed through axial compression tests and numerical simulations.First, based on the CFST stress-strain model, the constitutive model was modified to account for the effects of stiffening ribs, and a stress-strain relationship model for mold bag concrete was introduced. The simulation results demonstrate that the modified model can accurately predict the stress behavior of the fixed ends. Next, to characterize the overall mechanical response of the structure, a load-displacement relationship model was established. This model, which is closely related to the CFST strength grade, effectively captures changes in the structural performance.The results indicate that during loading, the CFST internal support system exhibits good stiffness and load-bearing capacity. With an increase in the concrete strength grade, the yield load increases by 12 %, and the ultimate strain decreases by 27.76 %, significantly enhancing the mechanical performance of the structure. This study not only deepens the understanding of the design principles for CFST internal support systems but also introduces new theoretical frameworks and calculation methods, providing strong support for engineering design with broad application prospects.

Seismic retrofitting of existing bridges has been in practice for years to meet the stringent seismic requirements set forward by revised design codes. For retrofitting, however, bridge piers are often prioritized while less attention is given to the bridge foundations, which are equally prone to damage under seismic loadings. The current work presents a series of experimental studies in assessing the performance of 2 x 2 pile groups reinforced with micropiles in terms of head-level stiffness and damping under low-to-high levels of static and dynamic loadings, encompassing the influence of loading-induced soil nonlinearity. Practical micropiles inclinations of 0 degrees, 5 degrees, and 10 degrees with respect to the vertical are studied. Experimental results reveal that the head-level stiffnesses of pile groups reinforced with micropiles, contrary to the general expectations, become smaller than the pile group without micropiles at higher levels of applied loading. To elucidate the governing mechanism for such experimentally obtained results, three-dimensional nonlinear finite-element analyses were carried out. Results from the numerical analyses support the experimental results, suggesting that the presence of micropiles may not always increase the head-level stiffness of soil-foundation systems, particularly at higher levels of applied loading where the soil nonlinearity generated at the vicinity of piles and micropiles governs the overall head-level stiffnesses.

A series of numerical simulations were completed to investigate the behavior of intact, fire -damaged, and Carbon Fiber -Reinforced Polymers (CFRP) retrofitted reinforced concrete (RC) bridge columns of varying sizes subjected to vehicle collisions. Three-dimensional finite element models of isolated RC columns and their foundation systems surrounded by soil volumes were developed using LS-DYNA. A comprehensive parametric study was carried out to investigate the effects of nine demand and design parameters on the performance of bridge columns. Studied parameters included: column diameter, column height, unconfined compressive strength, steel reinforcement ratio, fire duration, CFRP wrap thickness, wrapping configuration, vehicle 's mass, and vehicle 's speed. For each studied scenario, Peak Twenty-five Milli -second Moving Average ( PTMSA ) was employed to estimate the Equivalent Static Force ( ESF ) corresponding to each vehicle collision scenario. Resulting ESF s were then utilized to assess effectiveness of the current ESF approach available in the American Association of State Highway and Transportation Officials Load and Resistance Factor Design ( AASHTO-LRFD ) Bridge Design Specification for analyzing and helping design bridge columns under vehicle collision. Multivariate nonlinear regression analyses were used to derive an empirically based, simplified equation to predict the ESF that corresponds to a vehicle collision. Rather than constant design force, this equation established a correlation between ESF and kinetic energy, column axial capacity, and column height. Results indicated that the proposed equation is reliable and can accurately predict ESF s over a diverse range of collision scenarios that included intact, fire damaged, and CFRP retrofitted columns. To facilitate realistic implementation of the derived equation, an ESF assessment framework was also devised.

Accurate prediction of soil settlements induced by open caisson construction in sand is essential for safe and reliable delivery of critical underground urban infrastructure. This paper presents a novel prescriptive design approach using a neural network (NN) constrained by empirical relationships, referred to as an 'empiricism-constrained neural network'. The proposed approach is benchmarked using a traditional closed-form empirical expression. Both methods are calibrated using experimental data from reduced-scale laboratory testing for the prediction of surface and subsurface settlement trough shape and magnitude. The outcomes demonstrate that while both methods accurately capture the primary effect of caisson depth on surface and subsurface soil settlements, the NN approach exhibits superior prediction accuracy. These methods are developed in a form amenable for routine design use in industry and have the potential for broader applicability in other design scenarios, such as building damage assessment and risk-assessment exercises.

A standardized preparation process is proposed in this study for achieving optimal strength and vegetative properties in vegetated concrete, using Yunnan red soil as a growth substrate for plants. The porosity of vegetated concrete is a crucial factor influencing plant growth, while compressive strength is a significant mechanical property. To assess the strength and porosity of vegetated concrete, different design porosities (22%, 24%, 26%, 28%) and cement-to-aggregate ratios (4, 5, 6, 7) were utilized in the preparation of vegetated concrete samples. The shell-making and static-pressure-molding methods were optimized for specimen preparation. Analyzing the stress-strain full curve characteristics of vegetation-type concrete under different influencing factors, an in-depth investigation into its failure mechanism was conducted. It was determined that the design porosity and cement content significantly impact the concrete's performance, particularly in terms of 30-day compressive strength and effective porosity. Furthermore, an increase in the fly ash ratio led to an increase in porosity and a decrease in compressive strength, providing a certain guidance for optimizing concrete performance. Comparative analysis through vegetation experiments revealed that black rye grass exhibited favorable growth adaptability compared to other grass species.

The soil water characteristic curve describes the nonlinear relationship between soil water content and suction, which provides a basis for the study of soil-water interaction of unsaturated soil. The parameters of unsaturated soil can be obtained by measurement directly, this method, however, is usually costly and time-consuming. The soil water characteristic curve can be used to predict the mechanical properties of unsaturated soil, such as permeability coefficient and shear strength. Therefore, it is of great significance to obtain the soil water characteristic curve for the study of unsaturated soil properties. This paper summarizes the research of soil water characteristic curve fitting and its influencing factors. According to the different research perspectives, the content is divided into two parts: the fitting and the experimental acquisition of soil water characteristic curve. By combing the research results of the two parts, it is considered that the future research focus on soil water characteristic curve are: (1) obtain the appropriate pore size distribution function; (2) optimize the empirical model of soil water characteristic curve, so that the model can be more extensive; (3) Improving the accuracy of the database and how to better extract the characteristics of the test data is the key study of the soil-water characteristic curve prediction; (4) develop the study on soil-water interact under the coupled thermo-chemical-hydro- mechanical field.

This research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story steel structures considering four soil type effects. To prepare the dataset, 3584 incremental dynamic analysis (IDA) were performed on 64 structures. The research employs 6-, and 8-story structures to validate the AL-Ensemble ML model's effectiveness, showing it achieves the highest accuracy among conventional ML models, with an R-2 of 98.4%. Specifically, it accurately predicts the RID of floor levels in a 6-story structure with an accuracy exceeding 96.6%. Additionally, the programming code identifies the specific damaged floor level in a building, facilitating targeted local retrofitting instead of retrofitting the entire structure promising a reduction in retrofitting costs while enhancing prediction accuracy.