bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of the axial strain development curve of soft clay under dynamic triaxial conditions based on LSTM
Authors: Xiao Siqi , Wu Hao , Zhou Jie 
Published: May 2024

Abstract
The dynamic triaxial test results of soft clay show that a long-term cyclic traffic load lead to significant deformation of soft clay. Considering that the dynamic triaxial test data of soft clay under cyclic loading have apparent time series characteristics, the long short-term memory (LSTM) model can be used to predict the deformation law of soft clay. Through a dynamic triaxial test of Shanghai soft clay, time series data, such as axial stress, pore water pressure, and axial strain were obtained to establish the sample data of the LSTM model. The LSTM model is then used to predict the axial strain development curve. The results showed that the LSTM model had a good fitting precision for the sample data under different dynamic stresses and frequencies, and its accuracy was above 97% in all conditions. The prediction results can reproduce the fluctuation law of the axial strain under cyclic loads and can be used to study elastic and plastic strains. The research results can provide technical support for disaster prevention and control in soft clay.
Physics-informed machine learning of multi-source monitoring data sparsely measured within a 2D vertical cross-section
Authors: Tian Hua-Ming , Wang Yu 
Published: May 2024

Abstract
Conventional machine learning (ML) methods suffer from several limitations, e.g., overreliance on large training datasets and poor performance in extrapolating beyond observations. Geotechnical monitoring data at specific sites are multi-sourced (e.g., settlement and horizontal displacement data). Moreover, each source of monitoring data may vary temporally and be sparsely measured, e.g., within a two-dimensional (2D) geological cross-section. Thus, effectively learning multi-source and limited monitoring data is difficult using existing ML methods. To address this challenge, this study proposes a physics-informed ML method combining geotechnical numerical models (e.g., the finite element model (FEM)) with sparse dictionary learning (SDL) methods. It uses a range of probable numerical model results corresponding to multiple, temporally varying responses in a 2D spatial range to construct a “dictionary” in SDL and employs multi-source and limited monitoring data to identify a few “atoms” from the constructed dictionary to improve model predictions. An engineering project for embankment construction is used to illustrate the proposed approach. Our results indicate that the proposed approach effectively integrates FEM results with multi-source and sparsely measured monitoring data and improves the predictions of multiple spatio-temporally varying geotechnical responses significantly, particularly those at subsequent time steps and unmonitored spatial locations within a 2D vertical cross-section.
Semantic segmentation of large-scale segmental lining point clouds using 3D deep learning
Authors: Lin Wei , Sheil Brian , Xie Xiongyao , Zhang Yangbin , Cao Yuyang 
Published: May 2024

Abstract
It is highly challenging to comprehensively measure the deformations of large-scale tunnel structures because of the numerous individual segments of tunnel linings. Laser scanning and 3D deep learning (DL) can provide abundant geometric data and the ability to interpret such data automatically. To fill the gap in applying 3D DL to the segment-wise segmentation of large-scale tunnel point clouds, a new 3D dataset and an optimized DL network are presented in this paper. The point cloud dataset was manually annotated by labeling every individual segment and element. Subsequently, a 3D DL network capable of handling large-scale point clouds (approximately 106 points) was developed to perform the data experiments. The influences of network parameters, namely, the number of neighbor points, layer number, down-sampling ratio, and channel number, were investigated in the data experiments. The results indicate that the number of neighboring points and channel number have a notable influence on the segmentation performance, with the best mean interaction over union reaching 85.5%. This study provides preliminary insights into the 3D DL network design for large-scale tunnel point clouds.
A method to construct a main hole structural plane model based on advanced parallel adit geological body
Authors: Zhang Shuainan , Wu Wei , Zhang Min , Huang Yong , Fu Wei 
Published: May 2024

Abstract
The advanced parallel adit of tunnel refers to the adit parallel to the direction of the main hole. Generally it is spaced tens of meters from the axis of the positive hole. During the tunnel construction, the parallel adit is driven before the main hole. Main function of it is to provide geological reference for the subsequent excavation of the main hole. At the same time, it can also help improve the long-term construction and operation and maintenance efficiency of the main tunnel. In terms of providing geological prediction information for the main tunnel, the surrounding rock information disclosed in the advanced parallel adit excavation is mainly used for surrounding rock classification and geological sketch, so as to qualitative analysis the possible geological conditions when the positive tunnel is excavated near the distance where the advanced parallel adit is located. However, the above methods cannot accurately and quantitatively analysis the geological conditions that the main tunnel will face. The lack of research on the correlation between the main tunnel and the advanced parallel adit geological conditions results in the waste of the key factor of the horizontal geological information.In this paper, the improved DBSCAN clustering algorithm is used to group the dominant structural planes according to the attitude of rock and other information. Some non-primary structural planes are screened out. The average distance between the same group of structural planes is calculated according to the average attitude of the dominant structural plane. The structural planes that are close enough are fused. Then, the axial and radial projection was carried out to form a larger range of surrounding rock fracture models. When the main tunnel is excavated to the relevant area, the situation of the surrounding rock in front can be predicted according to the model, so that the countermeasures can be made in advance. The method in this paper is applied to a tunnel project with bidirectional single line and advanced parallel adit. The prediction results after tunnel excavated show that the method in this paper is reasonable and effective.
Laboratory assessment of shear strength parameters of sand amended via subsequent biopolymer-based soil treatment and enzyme-induced calcite precipitation combinations
Authors: Park Suhyuk , Park Jinwoo , Chang Ilhan 
Published: May 2024

Abstract
Current ground improvement practices predominantly rely on cement; however, interest in strengthening the ground in an environmentally sustainable manner is growing due to concerns regarding the environmental impact, such as the high CO2 footprint associated with cement usage. Biopolymer-based soil treatment (BPST) and enzyme-induced calcite precipitation (EICP) have emerged as promising methods for environmentally friendly ground improvement. While several studies have demonstrated the effectiveness of BPST and EICP individually, both methods have their limitations, which can be seen as complementary (for instance, EICP is more effective for coarse soils, whereas BPST is more effective for clayey soils). Recently, efforts have been made to combine BPST with EICP to address these limitations. This study considered four consecutive BPST-EICP combinations: 1) EICP alone, 2) BPST alone, 3) pre-EICP followed by post-BPST, and 4) pre-BPST followed by post-EICP. Laboratory experiments were conducted to assess the shear strength parameters (i.e., direct shear cohesion and friction angle) of sand treated using these four methods. The peak and residual shear strength parameters of the pre-EICP followed by post-BPST soil were the highest among all cases. However, each combination is suited to specific site conditions, such as soil type, groundwater conditions, and the purpose of ground improvement.
Analysis and intelligent prediction of subway train-induced vibration in the building with pile foundation
Authors: Tu W B , Shen L Q , Zhang X L , Liu L Y , Li J P , Zhang P F 
Published: May 2024

Abstract
The rapid development of subway construction inevitably leads to the subway lines traversing through or adjoining the buildings. However, current research primarily focuses on the train-induced ground-borne or building with shallow foundation vibrations, with a relatively limited investigation into the vibration pertaining to the building with pile foundation. A vibration analysis model of a building with pile foundation adjacent to a subway is established in this study using the finite element method, and the effects of various factors on the vibration of the building are analyzed, then the prediction of building vibration characteristics is also carried out by combining artificial intelligence methods. The results show that the building floor has an amplifying effect on the vibration acceleration level and Z-vibration level in the high frequency range, and the vibration energy attenuation at high frequencies is faster in the building with pile foundation compared to the building with shallow foundation. In the meantime, it shows that the GA-BP model is capable of effectively capturing the vibration characteristics of the building with pile foundation within the dominant frequency range. and the vibration of the building with pile foundation can be predicted not only by parameters such as train speed, soil and structure parameters but also by train-induced ground-borne vibration, which provides the possibility of simplifying the process of vibration prediction process and enhancing the practical application.
Numerical modelling of downward progressive landslides in sensitive clay
Authors: Zhang Y , Zhang X , Li X 
Published: May 2024

Abstract
Landslides in sensitive clays are catastrophic events that threaten life and property. When plastic strain occurs in sensitive clays, there is a significant reduction in the undrained shear strength. Consequently, even minor triggers can lead to multiple progressive landslides, causing extensive devastation. Investigating landslides in sensitive clay presents challenges, necessitating robust numerical methods to comprehend failure modes and collapse behaviors, particularly in the post-failure phase. Although several numerical methods have been utilized for simulations, most have primarily focused on upward progressive landslides. In this study, we adopted the nodal integration-based particle finite element method to simulate downward progressive landslides triggered by surcharge loads from an embankment near the crest in sensitive clay on a long slope. We also considered nonlinear strain softening in sensitive clays. The effects of the strain softening rate and strength gradient of the sensitive clay layer on the failure mechanisms and destruction extent were investigated. The simulation results reveal that a downward progressive landslide initiates with a localized flow slide surrounding the embankment and usually involves a combination of various failure modes.
Research and application of sinking-aided methods for circular open caisson under complex formation conditions: case study
Authors: Wang J , Zhen L , Fang T , Tao Y 
Published: May 2024

Abstract
Open caissons have been widely used owing to their good structural integrity and waterproofing performance, convenient construction, and low cost. However, sinking construction is affected by many factors, such as geological conditions, groundwater, designed depth, and self-weight. Improper measures may lead to severe subsidence of the surface and adjacent structures of an open caisson and even adversely affect its structure. This study using a large-diameter circular open caisson in the Jinshan Lake, Zhenjiang, first examines the causes of its substantial sinking and stagnation during construction. Owing to the different leading causes, sinking-aided methods differ. Therefore, these methods and their subsequent optimization schemes are established, and their effects are analyzed to provide a reliable reference for future related projects.
Experimental study on the stability of dike reinforced by steel sheet pile under storm surge
Authors: Zhang J L , Xie K L , Peng M 
Published: May 2024

Abstract
Over recent decades, upgrading dikes that defend against storm surges increasing in both intensity and frequency resulting from global warming, face challenges such as environmental impact and susceptibility to brittle failure. To address this issue, the steel sheet pile can be employed to improve the dike stability due to its resilience in withstanding substantial deformation. To investigate the reinforcement mechanism of the steel sheet pile, this study conducted three wave flume tests including the dike without configuration, the dike with single sheet pile, and the dike with double sheet pile. The dike stability and pile bending moment were analyzed based on the monitoring data. The experimental results showed that the single pile significantly improved the dike stability by reducing the seepage. The maximum bending moment for the single sheet pile occurred at approximately 1/3 of the pile depth, primarily due to its proximity to the wave impact area on the dike. The double pile further enhanced the dike stability primarily through the reduction of wave scour. The maximum bending moment for the double pile occurred at a lower position (2/3 of the pile depth) and was smaller due to the external force distribution and tie rod limitation.
Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles
Authors: Lv You , Zhang Ru , Zhang Anlin , Shen Yanjun , Ren Li , Xie Jing , Zhang Zetian , Zhang Zhilong , An Lu , Sun Junlong , Yan Zhiwei , Mi Ou 
Published: May 2024

Abstract
The combined impact of freeze-thaw cycles and dynamic loads significantly influences the long-term durability of rock engineering in high-cold regions. Consequently, investigating the dynamic compressive strength (DCS) of rocks subjected to freeze-thaw cycles has emerged as a crucial area of scientific research to advance rock engineering construction in cold regions. Presently, the determination of the DCS of rocks under freeze-thaw cycles primarily relies on indoor experiments. However, this approach has faced criticism due to its drawbacks, including prolonged duration, high costs, and reliance on rock samples. To address these limitations, the exploration of using artificial intelligence technology to develop more accurate and convenient DCS prediction models for rocks under freeze-thaw cycles is a promising attempt. In this context, this paper introduces a DCS prediction model for rocks under freeze-thaw cycles, which integrates the Sparrow Search Algorithm (SSA) with Random Forest (RF). Firstly, employing a dataset of 216 samples, Principal Component Analysis (PCA) is utilized to reduce the dimensionality of ten influential factors. Subsequently, five optimization algorithms are employed to optimize the hyperparameters of both the BP and RF algorithms. Finally, a comprehensive evaluation and comparative analysis are carried out to assess the predictive performance of the optimized model, using evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).The research findings demonstrate that the SSA-RF model exhibits the best predictive performance, surpassing the other nine models in terms of generalization. The prediction model proposed in this study has good applicability for predicting DCS of freeze-thaw rock in cold regions, and also provides new ideas for the combination of machine learning and rock mass engineering in cold regions.
Approach for Data-driven Controlling of Construction Projects using the example of Claim Management
Authors: Lenz L T , Schmidt J-L , Riechmann A , Gralla M 
Published: May 2024

Abstract
Current methods for controlling of construction projects are characterized by manual processes, which are failure-prone, tedious and for that time-consuming and cost-intensive. Furthermore, an additional challenge is caused by the absent dynamic in conventional controlling processes, which is of high importance to avoid damages and ensure a proactive management of a construction project. As construction projects are highly complex due to various influences which imply an enormous amount of information and are designated by dynamic processes, conventional controlling methods are insufficient and not able to meet these requirements. Therefore, current data-driven developments which can deal with an enormous amount of information are indispensable for a sufficient controlling of complex and dynamic projects. Accordingly, within this paper an approach for a data-driven controlling solution is presented, in which common data environments, databases and business intelligence applications are combined to ensure an efficient and proactive controlling. To investigate the advantages of business intelligence solutions for data-driven controlling the exemplary scenario of claim management controlling is used. It is shown that the user is able to speed up the processes and secure a proactive controlling by using a consistent database throughout the project. To achieve these targets an automatic data orchestration and visualization of the claim offers is exploited. Consequently, the claim manager is able to predict developments, identify risk potential, investigate KPIs or take actions to minimize negative effects e.g., time effort and costs. The requirements for these purposes are considered in a recommendation for action differentiated regarding the individual elements of a socio-technical system (human, organization, technology). The outlook includes an overview of potential technologies to ensure real-time analytics to shorten reaction and damage times related to the claims by immediately initiating countermeasures.
A Numerical Study on the Influence of External Magnetic field on Hydrogen Electric Arc Flow
Authors: Al Nasser M , Karimi-Sibaki E , Wu M , Redl C , Ishmurzind A , Voller N , Hackl G , Kharicha A 
Published: May 2024

Abstract
We present a 3D numerical model capable of simulating hydrogen electric arc. The numerical model couples the electromagnetic field with hydrogen plasma arc dynamics (flow and heat transfer). Electric arc operates under the conditions of atmospheric pressure using direct current (DC) in the presence of external magnetic field. It is found that arc instabilities emerge spontaneously near the cathode before propagating downwards along the arc jet. Also, spontaneous splitting into several arcs is observed before merging again into one arc. The horizontal magnetic field induces arc skewing and directional flow perpendicular to the applied magnetic field. However, the axial magnetic field induces a rotational flow and arc swirling.
Corrosion properties and mechanism of Ti43Zr27Mo5Cu10Be15 amorphous composites in various conditions
Authors: Lin Dan , Shang Jian , Zheng Pan 
Published: May 2024

Abstract
The in situ Ti43Zr27Mo5Cu10Be15 amorphous composites were investigated for their corrosion properties in solutions of NaCl, HCl, H2SO4 and NaOH. Electrochemical testing, SEM, EDS and XPS analyses revealed that pitting in NaCl and HCl solutions caused local surfaces damage. Amorphous matrix corrodes slightly in H2SO4 solution. Uniform corrosion occurred in NaOH solution without passive film formed, leading to the worst corrosion resistance. The optimal corrosion performances for NaCl and HCl solution are achieved at 0.5 mol l−1 and 0.75 mol l−1 separately which is related to the shortage of oxygen content in the solutions. While, the best corrosion performances for H2SO4 and NaOH solution are at 0.25 mol l−1. Moreover, the research on the effect of temperature was conducted in 3.5 wt% NaCl solution, it was found that Ti43Zr27Mo5Cu10Be15 amorphous composites exhibited good corrosion resistance at 298 K, while the E
corr declined with increasing solution temperature.
Enhancing photocatalytic performance of BiOI/TiO2 NTs nanocomposites through SILAR optimization for methylene blue degradation
Authors: Zaghdoudi M , Bennaceur J , Ouertani R 
Published: May 2024

Abstract
BiOI nano-leaves were deposited on to TiO2 nanotubes (NTs) using the Successive Ionic Layer Adsorption and Reaction (SILAR) technique, which was developed using the electrochemical anodization method. Various SILAR cycle numbers (three, five, and seven cycles) were employed in the experiment. The as-prepared nanocomposites (NCs) were characterized by several technique, the morphology of the elaborated NCs samples was examined using a S − 4800 field emission scanning electron microscope (SEM), the Reflectance and diffuse reflectivity of the NCs samples were measured by a Shimadzu UV-3100S spectrophotometer in the spectral range [300 –1200 nm] and the XRD diffraction was used to identify the crystalline structure of the processed BiOI/TiO2 NTs nanocomposites. A diffractometer with a Cu Kα anode (λ = 0.1542 nm) operating at 40 kV and 30 mA was used. The as-prepared NCs, specifically BiOI/TiO2 NTs, were designed for the photocatalytic degradation of methylene Blue (MB). X-ray diffraction analysis revealed the formation of the TiO2 anatase phase and polycrystalline BiOI films in all processed NCs. UV/Vis measurements indicated a shift in the nanocomposite’s active region from UV to visible light. The highest absorption and the lowest bandgap energy (Eg value, ∼2 eV) were observed in the NCs with 5 BiOI cycles. The photocurrent density reached 27 μA cm−2, approximately three times higher than the photocurrent density exhibited by TiO2 nanotubes under similar conditions. The optimal photocatalytic rate was achieved with BiOI/TiO2 NCs processed after five SILAR cycles.
Towards a realistic model for cavity-enhanced atomic frequency comb quantum memories
Authors: Taherizadegan Shahrzad , Davidson Jacob H , Kumar Sourabh , Oblak Daniel , Simon Christoph 
Published: May 2024

Abstract
Atomic frequency comb (AFC) quantum memory is a favorable protocol in long distance quantum communication. Putting the AFC inside an asymmetric optical cavity enhances the storage efficiency but makes the measurement of the comb properties challenging. We develop a theoretical model for cavity-enhanced AFC quantum memory that includes the effects of dispersion, and show a close alignment of the model with our own experimental results. Providing semi-quantitative agreement for estimating the efficiency and a good description of how the efficiency changes as a function of detuning, it also captures certain qualitative features of the experimental reflectivity. For comparison, we show that a theoretical model without dispersion fails dramatically to predict the correct efficiencies. Our model is a step forward to accurately estimating the created comb properties, such as the optical depth inside the cavity, and so being able to make precise predictions of the performance of the prepared cavity-enhanced AFC quantum memory.