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.

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.

Intelligent construction has become an inevitable trend in the development of the construction industry. In the excavation project, using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process. An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm (AVOA) was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation. We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations. First, we exploratively analyzed these data to discover the relationship between features. We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection. The hyperparameters for all models in evaluation are optimized using AVOA, and then the optimized models are assembled into a unified framework for fairness assessment. The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well (RMSE=1.84, MAE=1.18, R^2=0.9993) and cross-validation (RMSE=2.65\pm 1.54, MAE=1.17\pm 0.23, R^2=0.998\pm 0.002). In the end, in order to improve the transparency and usefulness of the model, we constructed an interpretable model from both global and local perspectives.

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.

Though a comprehensive in situ measurement project, the performance of a deep pit-in-pit excavation constructed by the top-down method in seasonal frozen soil area in Shenyang was extensively examined. The measured excavation responses included the displacement of capping beam and retaining pile, settlement of ground surface, and deformation of metro lines. Based on the analyses of field data, some major findings were obtained: 1) the deformations of retaining structures fluctuated along with the increase of temperature, 2) the deformation variation of retaining structures after the occurrence of thawing of seasonal frozen soil was greater than that in winter, although the excavation depth was smaller than before, 3) the influence area of ground settlement was much smaller because of the features of seasonal frozen sandy soil, 4) the displacement of metro line showed a significant spatial effect, and the tunnel lining had an obviously hogging displacement pattern, and 5) earth pressure redistribution occurred due to the combined effects of freezing-thawing of seasonal frozen soil and excavation, leading to the deformation of metro line. The influence area of ground settlement was obviously smaller than that of Shanghai soft clay or other cases reported in literatures because of special geological conditions of Shenyang. However, the deformation of metro lines was significantly lager after the thawing of the frozen soil, the stress in deep soil was redistributed, and the metro lines were forced to deform to meet a new state of equilibrium.

The study aims to develop machine learning-based mechanisms that can accurately predict the axial capacity of high-strength concrete-filled steel tube (CFST) columns. Precisely predicting the axial capacity of a CFST column is always challenging for engineers. Using artificial neural networks (ANNs), random forest (RF), and extreme gradient boosting (XG-Boost), a total of 165 experimental data sets were analyzed. The selected input parameters included the steel tensile strength, concrete compressive strength, tube diameter, tube thickness, and column length. The results indicated that the ANN and RF demonstrated a coefficient of determination (R²) value of 0.965 and 0.952 during the training and 0.923 and 0.793 during the testing phase. The most effective technique was the XG-Boost due to its high efficiency, optimizing the gradient boosting, capturing complex patterns, and incorporating regularization to prevent overfitting. The outstanding R² values of 0.991 and 0.946 during the training and testing were achieved. Due to flexibility in model hyperparameter tuning and customization options, the XG-Boost model demonstrated the lowest values of root mean square error and mean absolute error compared to the other methods. According to the findings, the diameter of CFST columns has the greatest impact on the output, while the column length has the least influence on the ultimate bearing capacity.

As urban construction continues to develop and automobile ownership rises, parking shortages in cities have become increasingly acute. Given the limited availability of land resources, conventional underground garages and parking buildings no longer suffice to meet the growing demand for parking spaces. To address this dilemma, underground parking shaft (UPS) has emerged as a highly regarded solution. This study provides an overview of the layout scheme, structural design approaches, and construction techniques for UPS, focusing on the characteristics of intensive construction demonstrated in the project located in the Jianye District of Nanjing. Compared to conventional vertical shaft garage construction methods, this assembly parking shaft offers advantages such as a smaller footprint, higher prefabrication rate, shorter construction period, and reduced environmental impact. It presents an efficient approach for the intensive construction of urban underground spaces, particularly in areas with limited land and complex environments, showing promising prospects for widespread application.

This paper presents a calculation method that evaluates the extent of disturbance based on structural safety limits. Additionally, it summarizes the assessment methods for construction disturbance zones in shield tunneling near pile foundations, urban ground structures, and underground structures. Furthermore, taking the construction of the Chengdu Jinxiu Tunnel under bridges and urban pipelines as the engineering background, a study on the disturbance zoning of adjacent structures was conducted. The most intense disturbance occurs within one week of the tunnel underpass process, and it has a significant impact within a range of two times the tunnel diameter along the tunnel axis. The bridge pile and bridge deck experience less disturbance from tunnel approaching construction, with a maximum disturbance zone characterized as medium disturbance. On the other hand, underground pipelines are subjected to more significant disturbances from tunnel construction, with a maximum disturbance zone classified as strong disturbance. The implementation of “bridge pile sleeve valve pipe grouting & underground pipeline ground grouting & tunnel advance grouting” in the field effectively limits the vertical settlement of bridges and pipelines, resulting in a decrease of approximately 0.1 in disturbance level for the structures. The disturbance zoning method can assess tunnel disturbance with structures, identify high-risk interference locations, and facilitate targeted design reinforcement solutions.

Collecting and analyzing vibration signals from structures under time-varying excitations is a non-destructive structural health monitoring approach that can provide meaningful information about the structures’ safety without interrupting their normal operations. This paper develops a novel framework using prompt engineering for seamlessly integrating users’ domain knowledge about vibration signals with the advanced inference ability of well-trained large language models (LLMs) to accurately identify the actual states of structures. The proposed framework involves formulating collected data into a standardized form, utilizing various prompts to gain useful insights into the dynamic characteristics of vibration signals, and implementing an in-house program with the help of LLMs to perform damage detection. The advantages, as well as limitations, of the proposed method are qualitatively and quantitatively assessed through two realistic case studies from literature, demonstrating that the present method is a new way to quickly construct practical and reliable structural health monitoring applications without requiring advanced programming/mathematical skills or obscure specialized programs.