AI in civil engineering最新文献

{"title":"Prediction of permeability of amended soil using ensembled artificial intelligence models","authors":"Ankit Kumar,&nbsp;Rohit Ahuja","doi":"10.1007/s43503-025-00052-y","DOIUrl":"10.1007/s43503-025-00052-y","url":null,"abstract":"<div><p>Soil permeability is a critical parameter that dictates the movement of water through soil, and it impacts processes such as seepage, erosion, slope stability, foundation design, groundwater contamination, and various engineering applications. This study investigates the permeability of soil amended with waste foundry sand (WFS) at a replacement level of 10%. Permeability measurements are conducted for three distinct relative densities, spanning from 65% to 85%. The dataset compiled from these measurements is employed to develop ensemble artificial intelligence (AI) models. Specifically, four regressor AI models are considered: Nearest Neighbor (NNR), Decision Tree (DTR), Random Forest (RFR) and Support Vector Machine (SVR). These models are enhanced with four distinct base learners: Gradient Boosting (GB), Stacking Regressor (SR), AdaBoost Regressor (ADR), and XGBoost (XGB). The input parameters include fraction of base sand (BS), fraction of waste foundry sand (WFS), relative density (RD), duration of flow (T), quantity of flow (Q) and permeability (k), totalling 165 data points. Through comparative analysis, the Gradient Boost with Decision Tree (GB-DTR) model is found to be best-performed model, with R² = 0.9919. Sensitivity analysis reveals that Q is the most influential input parameter in predicting soil permeability.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00052-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of agro-industrial by-products in expansive soil amelioration: design of experiment approach","authors":"Imoh Christopher Attah","doi":"10.1007/s43503-025-00050-0","DOIUrl":"10.1007/s43503-025-00050-0","url":null,"abstract":"<div><p>The utilization of waste residues for soil amelioration is becoming increasingly popular in the construction industry due to its potential for effective waste management and resource utilization. This practice is of utmost importance for the sustainable development of nations, as it offers both environmental protection and economic benefits. In this study, we investigate the sustainable incorporation of Design of Experiment (DOE) to optimize the use of binary additives for enhancing expansive soil. The selected binary additives for this study are calcium carbide residue (CCR) and palm oil fuel residue (POFR). A total of twenty different mix designs were prepared using various combinations of CCR, POFR, water, and soil, following the Scheffe’s DOE strategy. To evaluate the performance and effectiveness of the additives, mechanical testing, including durability and unconfined compressive strength tests, was conducted. The results showed peak values of 58% for durability and 735 kN/m² for unconfined compressive strength (UCS). Additionally, the analysis of variance and student t-test, which are standard techniques for assessing the goodness of fit, were applied to statistically analyse the mathematical models and validate their adequacy and validity. Microstructural experiments, involving scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR), were performed on the natural soil and soil treated with the optimal level of additives. The SEM analysis confirmed the formation of new compounds resulting from the incorporation of CCR-POFR mixtures, while the FTIR analysis validated the presence of different molecular functional groups in the treated soil.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00050-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive analysis of structural parameters influencing the fundamental period of steel-braced RC buildings using machine learning interpretability","authors":"Taimur Rahman,&nbsp;Md. Farhad Momin,&nbsp;Afra Anam Provasha","doi":"10.1007/s43503-025-00051-z","DOIUrl":"10.1007/s43503-025-00051-z","url":null,"abstract":"<div><p>The accurate prediction of the fundamental period of steel-braced reinforced concrete (RC) buildings is crucial for optimizing seismic design and ensuring structural safety. Traditionally, empirical formulas provided by building codes such as Eurocode 8 and ASCE 7–22 primarily rely on building height to estimate the fundamental period. However, these height-based models often overlook the significant influence of other structural parameters, such as bracing configurations, bracing lengths, and material properties. This study addresses these limitations by offering a comprehensive evaluation of the factors affecting the fundamental period of steel-braced RC buildings, using advanced computational techniques for more precise and interpretable predictions. A dataset comprising 17,280 building models with varied structural configurations was generated using computational simulations. Key parameters, including total building height, bracing type, bracing length, and building dimensions, were systematically varied. The study utilized machine learning techniques and employed SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) plots as post-hoc interpretability tools to analyze the contributions of structural parameters. Results show that total building height remains the dominant factor, contributing approximately 45% to the predicted fundamental period, while bracing length and bracing type significantly influence the period, reducing it by up to 20%. The inclusion of these parameters improves prediction accuracy and reveals limitations in existing height-based formulas. The study concludes that height alone is insufficient for accurate prediction of the fundamental period in steel-braced RC buildings. Incorporating bracing systems and other structural factors is essential for more reliable seismic design. These findings contribute to the development of more resilient building codes and enhanced seismic performance.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00051-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143581225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soft computing approaches for forecasting discharge over symmetrical piano key weirs","authors":"Abdelrahman Kamal Hamed,&nbsp;Mohamed Kamel Elshaarawy","doi":"10.1007/s43503-024-00048-0","DOIUrl":"10.1007/s43503-024-00048-0","url":null,"abstract":"<div><p>Piano Key Weir (PKW) is an advanced hydraulic structure that enhances water discharge efficiency and flood control through its innovative design, which allows for higher flow rates at lower upstream levels. Accurate discharge prediction is crucial for PKW performance within various water management systems. This study assesses the efficacy of Artificial-Neural-Network (ANN) and Gene-Expression-Programming (GEP) models in improving discharge prediction for symmetrical PKWs. A comprehensive dataset comprising 476 experimental records from previously published studies was utilized, considering a range of geometric and fluid parameters (PKW key widths, PKW height, and upstream head). In the training stage, the ANN model demonstrated a superior determination coefficient (R²) of 0.9997 alongside a lower Mean Absolute Percentage Error (MAPE) of 0.74%, whereas the GEP model yielded an R² of 0.9971 and a MAPE of 2.36%. In the subsequent testing stage, both models displayed a high degree of accuracy in comparison to the experimental data, attaining an R² value of 0.9376. Furthermore, SHapley-Additive-exPlanations and Partial-Dependence-Plot analyses were incorporated, revealing that the upstream head exerted the greatest influence on the discharge prediction, followed by PKW height and PKW key width. Therefore, these models are recommended as reliable, robust, and efficient tools for forecasting the discharge of symmetrical PKWs. Additionally, the mathematical expressions and associated script codes developed in this study are made accessible, thus providing hydraulic engineers and researchers with the means to perform rapid and accurate discharge predictions.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00048-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature fusion of single and orthogonal polarized rock images for intelligent lithology identification","authors":"Wen Ma,&nbsp;Tao Han,&nbsp;Zhenhao Xu,&nbsp;Peng Lin","doi":"10.1007/s43503-025-00049-7","DOIUrl":"10.1007/s43503-025-00049-7","url":null,"abstract":"<div><p>This paper presents an intelligent lithology identification method that utilizes the feature fusion of single polarized and orthogonal polarized rock images. The traditional thin section identification method heavily relies on manual expertise, leading to subjective results and requiring significant time and labor. To overcome these limitations, we establish a microscopic feature fusion model using a convolutional neural network (CNN). This model leverages the complementarity information from single polarized and orthogonal polarized features. By extracting features from microscopic rock images using convolutional kernels and integrating multi-feature information at both the input and feature levels, the proposed method enhances the classification accuracy of the model, providing a more efficient and objective solution for lithology identification. To evaluate the identification performance, several metrics including accuracy (<i>Acc</i>), precision (<i>P</i>), recall (<i>R</i>), <i>F1-score</i>, and a confusion matrix are employed. The results demonstrate that the fusion model achieved a maximum accuracy of 98.66% on the testing set, representing a 4.91% improvement over using single polarized images alone and a 1.55% improvement over orthogonal polarized images alone. The integration of advanced deep learning models with microscopic image analysis techniques enables researchers and non-geologists to automate the identification and classification of extensive rock sample datasets efficiently. Moreover, the proposed method proves particularly useful in cases with complex mineral compositions and similar structures, as it provides more reliable and accurate analytical results.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-025-00049-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of rock strength parameters from petrological contents using tree-based machine learning techniques","authors":"Javid Hussain,&nbsp;Xiaodong Fu,&nbsp;Jian Chen,&nbsp;Nafees Ali,&nbsp;Sayed Muhammad Iqbal,&nbsp;Wakeel Hussain,&nbsp;Altaf Hussain,&nbsp;Ahmed Saleem","doi":"10.1007/s43503-024-00047-1","DOIUrl":"10.1007/s43503-024-00047-1","url":null,"abstract":"<div><p>The demand for construction materials in Pakistan has experienced a significant increase, particularly due to the China-Pakistan Economic Corridor (CPEC) project, which necessitates substantial amounts of resilient resources for infrastructure development. Parameters of rock strength, including uniaxial compressive strength (UCS), Young’s modulus (E), and Poisson’s ratio (ν), are critical attributes of rock materials vital for applications such as rock slope stability assessment, tunnel construction, and foundation design. Conventionally, the measurement of UCS, E, and ν in laboratory settings resource-intensive, requiring considerable time and financial investment. This study proposes to provide a comprehensive assessment framework using an adaptive boosting machine (AdaBoost), extreme gradient boosting machine (XGBoost), and category gradient boosting machine (CatBoost), to indirectly estimate UCS, E, and ν through streamlined mineralogical analyses. The performance of the boosting trees was analyzed using Taylor diagrams and a suite of five regression metrics: coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), variance accounted for (VAF), and the A-20 index. The results indicate that the proposed boosting trees robust predictive capabilities for the constructed database. Notably, AdaBoost demonstrated the highest efficacy in predicting the strength of carbonate rock, achieving R² values of 0.98, 0.99, and 0.97, with the lowest RMSE values of 0.3164, 0.63, and 0.18, for UCS, E, and ν, respectively. Moreover, variable importance analysis highlighted that the presence of micrite and calcite has a significant impact on predicting UCS, E, and ν of carbonate rock. Furthermore, the AdaBoost model was validated using an independent dataset, which corroborated its predictive reliability. In conclusion, the proposed models present a highly effective methodology for the indirect prediction of essential mechanical properties of carbonate rocks, offering substantial time and cost efficiencies compared to traditional laboratory techniques.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00047-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143373273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of sustainability assessment of geopolymer concrete through AI-based life cycle analysis","authors":"V. Ramesh,&nbsp;B. Muthramu,&nbsp;D. Rebekhal","doi":"10.1007/s43503-024-00045-3","DOIUrl":"10.1007/s43503-024-00045-3","url":null,"abstract":"<div><p>Geopolymer concrete is acknowledged as a sustainable alternative to conventional Portland cement concrete owing to its ability to reduce carbon emissions and reutilize industrial by-products. This paper reviews the application of Artificial Intelligence-based Life Cycle Analysis (LCA) techniques in the sustainability assessment of geopolymer concrete. The assessment covers the entire life cycle of geopolymer concrete, spanning from the extraction of raw materials to its ultimate disposal, with a particular focus on its environmental, economic, and social impacts. The incorporation of AI techniques into the LCA process offers notable advantages, such as the efficient management of large datasets, enhancement of data quality, prediction of environmental impacts, and facilitation of informed decision-making. Key sustainability metrics to be considered include environmental impacts such as carbon footprint and energy consumption, economic factors like cost-effectiveness, as well as social implications. The amalgamation of AI within the LCA framework provides a comprehensive and efficient approach to evaluating the sustainability of geopolymer concrete, thereby facilitating its application in sustainable construction practices.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00045-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on slope stability assessment methods: a comparative analysis of limit equilibrium, finite element, and analytical approaches for road embankment stabilization","authors":"Chebou Nkenwoum Gael,&nbsp;Mambou Ngueyep Luc Leroy,&nbsp;Fokam Bobda Christian","doi":"10.1007/s43503-024-00046-2","DOIUrl":"10.1007/s43503-024-00046-2","url":null,"abstract":"<div><p>In this study, a comprehensive assessment of slope failure risk in man-made slopes was conducted, focusing specifically on the embankments in the excavated regions along the Tibati-Sengbe road in the Adamawa region of Cameroon. The primary objective of this study was to analyze the stability of these slopes and determine the safety factors that should be considered in their stabilization. To achieve this goal, a field survey was conducted to identify and characterize the areas at risk. The stability assessment was performed employing sophisticated numerical methods, including the Limit Equilibrium Method (LEM) utilizing the Bishop Method, the Finite Element Method (FEM) through the Plaxis Method, and the Analytical Method (AM) based on Taylor's Abacus. Ten slopes with homogeneous soil composition but varying geotechnical and geometric properties were selected as the objects for simulations, which were performed using the software packages ROCSCIENCE (Phase 2) for LEM and PLAXIS for FEM. The results indicated a high degree of consistency between the FEM and LEM methodologies, with an R² correlation approaching 1 in their comparison. Nonetheless, the AM yielded conflicting results in 60% of cases, emphasizing the fundamental significance of numerical methods in evaluating slope stability. The findings of this study discredit the effectiveness of analytical methods in determining safety factor calculations and highlight the accuracy and reliability of the FEM and LEM techniques given their consistent results.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00046-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven analysis in 3D concrete printing: predicting and optimizing construction mixtures","authors":"Rodrigo Teixeira Schossler,&nbsp;Shafi Ullah,&nbsp;Zaid Alajlan,&nbsp;Xiong Yu","doi":"10.1007/s43503-024-00044-4","DOIUrl":"10.1007/s43503-024-00044-4","url":null,"abstract":"<div><p>Accurately predicting 3D concrete printing (3DCP) properties through the utilization of machine learning holds promise for advancing cost-effective, eco-friendly construction practices that prioritize safety, reliability, and environmental sustainability. In this study, a comprehensive exploration of seven regression models was undertaken, complemented by the application of Bayesian optimization techniques to forecast critical metrics such as compressive strength, pump speed, and carbon footprint within the realm of 3DCP technology. Drawing upon a compilation of various 3DCP mixtures sourced from existing literature, an intricate carbon footprint calculation methodology was devised, resulting in the establishment of a bespoke database tailored to the study’s objectives. The performance evaluation of the developed models was conducted through the analysis of key statistical indicators, including <i>R</i>², RMSE, MAE, and Pearson correlation. To enhance the robustness and generalizability of the models, a rigorous tenfold cross-validation strategy coupled with a strategic introduction of noise was employed during the validation process. The incorporation of Shapley Additive Explanations (SHAP) analysis provided insightful interpretability into the predictive capabilities of the models, enabling a nuanced understanding of the underlying relationships between input variables and target outputs. Furthermore, the application of multi-objective optimization techniques facilitated judicious decision-making processes, enabling the identification of optimal 3DCP mixture compositions that concurrently enhance performance metrics, reduce operational costs, and mitigate CO₂ emissions.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00044-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VAGen: waterbody segmentation with prompting for visual in-context learning","authors":"Jiapei Zhao,&nbsp;Nobuyoshi Yabuki,&nbsp;Tomohiro Fukuda","doi":"10.1007/s43503-024-00042-6","DOIUrl":"10.1007/s43503-024-00042-6","url":null,"abstract":"<div><p>Effective water management and flood prevention are critical challenges encountered by both urban and rural areas, necessitating precise and prompt monitoring of waterbodies. As a fundamental step in the monitoring process, waterbody segmentation involves precisely delineating waterbody boundaries from imagery. Previous research using satellite images often lacks the resolution and contextual detail needed for local-scale analysis. In response to these challenges, this study seeks to address them by leveraging common natural images that are more easily accessible and provide higher resolution and more contextual information compared to satellite images. However, the segmentation of waterbodies from ordinary images faces several obstacles, including variations in lighting, occlusions from objects like trees and buildings, and reflections on the water surface, all of which can mislead algorithms. Additionally, the diverse shapes and textures of waterbodies, alongside complex backgrounds, further complicate this task. While large-scale vision models have typically been leveraged for their generalizability across various downstream tasks that are pre-trained on large datasets, their application to waterbody segmentation from ground-level images remains underexplored. Hence, this research proposed the Visual Aquatic Generalist (VAGen) as a countermeasure. Being a lightweight model for waterbody segmentation inspired by visual In-Context Learning (ICL) and Visual Prompting (VP), VAGen refines large visual models by innovatively adding learnable perturbations to enhance the quality of prompts in ICL. As demonstrated by the experimental results, VAGen demonstrated a significant increase in the mean Intersection over Union (mIoU) metric, showing a 22.38% enhancement when compared to the baseline model that lacked the integration of learnable prompts. Moreover, VAGen surpassed the current state-of-the-art (SOTA) task-specific models designed for waterbody segmentation by 6.20%. The performance evaluation and analysis of VAGen indicated its capacity to substantially reduce the number of trainable parameters and computational overhead, and proved its feasibility to be deployed on cost-limited devices including unmanned aerial vehicles (UAVs) and mobile computing platforms. This study thereby makes a valuable contribution to the field of computer vision, offering practical solutions for engineering applications related to urban flood monitoring, agricultural water resource management, and environmental conservation efforts.</p></div>","PeriodicalId":72138,"journal":{"name":"AI in civil engineering","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43503-024-00042-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}