Asian Journal of Civil Engineering最新文献

{"title":"Hybrid U-shaped steel dampers for advancing seismic isolation in building structures","authors":"Maheshwari S. Pise,&nbsp;D. V. Wadkar","doi":"10.1007/s42107-025-01433-5","DOIUrl":"10.1007/s42107-025-01433-5","url":null,"abstract":"<div><p>Modern seismic design codes prioritize structural resilience by accommodating inelastic deformation, especially in steel and reinforced concrete (RC) buildings. While this approach allows for ductile behaviour under strong earthquakes, it often results in structural damage. To reduce such damage, passive energy dissipation systems are increasingly utilized. This study introduces an innovative base isolation technique for RC frame structures, employing a hybrid U-shaped damper that integrates steel, shape memory alloy (SMA), and a rubber core. The U-shaped components, made from steel and SMA, exhibit elastoplastic and super elastic behaviour, respectively, enhancing energy absorption and self-centring capabilities. The rubber core, modelled using the hyper elastic Ogden formulation, contributes flexible isolation and nonlinear damping. Positioned at the base, the hybrid system enhances seismic performance by combining the isolating properties of rubber with the damping and recentring benefits of steel-SMA elements. Nonlinear time history analyses using four earthquake records confirmed that the hybrid isolator significantly reduces structural responses compared to systems without it.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 10","pages":"4433 - 4440"},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated data-driven optimization and microstructural modeling of nano-silica enhanced cement–fly ash–lime wall panels for prefabricated construction","authors":"Shradhesh R. Marve,&nbsp;Sumit R. Punam,&nbsp;Shady Gomaa Abdulaziz,&nbsp;Lowlesh N. Yadav,&nbsp;Sanket G. Padishalwar,&nbsp;Tejas R. Patil,&nbsp;Nischal Puri,&nbsp;Rohit Pawar,&nbsp;Amit Pimpalkar,&nbsp;Mayuri A. Chandak","doi":"10.1007/s42107-025-01440-6","DOIUrl":"10.1007/s42107-025-01440-6","url":null,"abstract":"<div>\u0000 \u0000 <p>As the world moves towards rapid urbanization, there arises a huge need for lightweight, high-strength, and low-cost prefabricated wall panels. Traditional cement-based systems show drawbacks with extremely high porosity along with limited early-age performance and poor microstructural control, especially with the incorporation of supplementary cementitious materials. Most optimization methods deal with strength only, without simultaneous control of perforation, microstructure, and practical constraints such as workability and cost. There is little understanding of microstructure-property relationships in terms of ternary blends modified with silica nanoparticles. The proposed work presents a complete, data-driven, multi-scale modeling framework for designing and optimizing cement-fly ash-lime wall panels augmented with silica nanoparticles. The hybrid machine learning-finite element surrogate modeling (ML-FEM-SM) approach combines the finite element simulation of microstructural stress and porosity evolution with machine learning regression to allow efficient prediction of compressive strength and pore distribution (R² ≈ 0.94, porosity error &lt; 5). This is complemented by MD-MDFMBE where multimodal data fusion entails the integration of FTIR spectra, thermal curing images, and early mechanical data from transformer networks for non-destructive early prediction of strength and shrinkage with ± 1.5 MPa accuracy. Microstructure GAN production (µGAN) synthetic SEM images are of high fidelity for virtual mix validation (SSIM &gt; 0.92). Constrained Multi-Objective Bayesian Optimization (MOBO-C) identified Pareto-optimal mixes under cost and flowability restrictions. Persistent Homology-Based Clustering (PHMC) is now classifying microstructural images into strength-correlated topological clusters (R² ≈ 0.89). The merged framework significantly improves the capabilities of mixing design for pre-casting quality control, deeper microstructure understanding, and performance-driven classification into advanced prefab materials.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 11","pages":"4567 - 4580"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based prediction of high-strength concrete compressive strength incorporating limestone aggregates using ensemble and pruned tree models","authors":"Akshat Mahajan,&nbsp;Pushpendra Kumar Sharma","doi":"10.1007/s42107-025-01445-1","DOIUrl":"10.1007/s42107-025-01445-1","url":null,"abstract":"<div><p>Accurate prediction of compressive strength is vital for ensuring the structural reliability and quality control of High-Strength Concrete (HSC). This study presents a data-driven modelling framework to predict the compressive strength of HSC incorporating varying proportions of limestone and natural aggregates, under different curing durations and ultimate loading conditions. Four tree-based machine learning models M5P, Reduced Error Pruning Tree (REP Tree), Random Tree (RT), and Random Forest (RF), were applied to a dataset comprising 123 experimental samples. The compressive strength served as the target output. Among the models, the ensemble-based Random Forest model achieved the highest prediction accuracy, with a training phase performance of CC = 0.9998, MAPE = 0.1161, RMSE = 0.2516, rRMSE = 0.23%, and NSEC = 0.9995, while testing metrics remained equally robust with CC = 0.9997, MAPE = 0.2881, RMSE = 0.3758, rRMSE = 0.38%, and NSEC = 0.9994. Sensitivity analysis using the Cosine Amplitude Method (CAM) revealed that ultimate load is the most influential input feature, with a sensitivity coefficient R_i=0.9999, indicating its dominant role in compressive strength development. Model performance was further substantiated through box plots, Taylor diagrams, and residual error visualizations. The findings support the use of Random Forest as a powerful tool for predicting the strength of HSC with blended aggregate systems, offering practical insights for performance-driven concrete design.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 11","pages":"4595 - 4613"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weighted-ensemble machine learning for simultaneous non-destructive prediction of rebound number and ultrasonic pulse velocity in concrete","authors":"Neha Sharma,&nbsp;Arvind Dewangan,&nbsp;Neelaz Singh,&nbsp;Devjani Bhattacharya,&nbsp;Sagar Paruthi,&nbsp;Rupesh Kumar Tipu","doi":"10.1007/s42107-025-01455-z","DOIUrl":"10.1007/s42107-025-01455-z","url":null,"abstract":"<div><p>Non-destructive testing (NDT) techniques such as the rebound hammer (yielding rebound number, RN) and ultrasonic pulse velocity (UPV) are widely used to infer concrete strength without damaging specimens, yet their standalone accuracy remains limited. In this study, we propose a weighted-ensemble machine learning framework that simultaneously predicts RN and UPV based on mix design parameters (cement, aggregates, water–cement ratio, admixtures) and curing age. Six traditional regressors–ElasticNet, SVR, KNN, Random Forest, XGBoost, and LightGBM–were each tuned via Optuna hyperparameter optimization. Ensemble weights were derived from inverse-RMSE scores on out-of-fold validation. On a hold-out test set of 30 specimens, the ensemble achieved RMSE = 0.83 and <span>(R^2)</span> = 0.94 for RN, and similarly strong performance for UPV, representing a 40–50% improvement over individual models. We further quantify prediction uncertainty with 95% bootstrap intervals (empirical coverage <span>(&gt; 90%)</span>) and interpret model behavior via SHAP and Sobol sensitivity analyses. Feature attributions reveal that nonlinear interactions–particularly second-order terms of aggregate contents and the water–cement ratio–dominate predictions, while sensitivity indices confirm cement dosage and <span>(w/c)</span> ratio as primary drivers. This integrated approach offers highly accurate, well-calibrated predictions and actionable insights for NDT-based quality control in concrete construction.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 11","pages":"4775 - 4796"},"PeriodicalIF":0.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-output machine learning techniques to predict strength characteristics of nano graphene oxide reinforced cement composites","authors":"S. K. Lal Mohiddin,&nbsp;B. Yashwanth,&nbsp;D. Ravi Parsad","doi":"10.1007/s42107-025-01437-1","DOIUrl":"10.1007/s42107-025-01437-1","url":null,"abstract":"<div><p>The use of nano graphene oxide (GO) in cement composites has shown tremendous potential for improving strength and performance characteristics. The impact of the addition of GO in concrete remains uncertain due to the interaction of the mix ingredients with the graphene oxide. To examine the influence of multiple coupling parameters on forecasting the mechanical properties using traditional experimental methods are cumbersome. In this study, Machine Learning (ML) approaches are used to investigate the intricate relationship between the multiple influencing parameters on the mechanical properties of GO reinforced cement composites. A comprehensive collection of 260 datasets related to GO, with 10 input parameters, was collected to train and test the machine learning models. Different Machine Learning techniques were applied to predict the multi-output parameters simultaneously. The SHapley Additive exPlanations approach identified the most influential parameters of the composite strength characteristics. The results revealed that the XGBoost model delivered highly accurate predictions, with lower RMSE, MSE, and MAE values, and a higher R² value of 0.9 compared to other ML models. Multi-Output Machine Learning Techniques have proven to be a quick and cost-effective solution, an alternative to time-consuming traditional tests.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 11","pages":"4517 - 4533"},"PeriodicalIF":0.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of optimized machine learning tool for predicting compressive strength of concrete","authors":"Kshitish Parida,&nbsp;Laren Satpathy,&nbsp;Amar Nath Nayak","doi":"10.1007/s42107-025-01463-z","DOIUrl":"10.1007/s42107-025-01463-z","url":null,"abstract":"<div><p>Optimizing concrete mix design is essential for advancing sustainable construction practices. Conventional methods for evaluating the compressive strength (CS) of concrete, a critical mechanical property, are often time-intensive and resource-demanding. This study investigates the application of machine learning (ML) models to predict CS of concrete more efficiently, utilizing the Python interface on Google Colab. Multiple regression models have been assessed using performance metrics including mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and the coefficient of determination (R²). A stacked regression (SR) model has been developed by integrating 14 base models, with CatBoost (CB) employed as the meta-learner. The models have been trained and tested on a dataset comprising 1315 samples collected from the Concrete Laboratory at Veer Surendra Sai University of Technology (VSSUT), Burla, using an 80/20 train-test split. To enhance model performance, hyper-parameter tuning via Grid Search and validation through K-Fold cross-validation have been employed. The optimized SR-CB model has achieved superior predictive accuracy, recording an RMSE of 1.95 and an R² of 0.93. Furthermore, SHAP and LIME analyses have been conducted to interpret the feature importance and model behaviour. The model’s generalizability has been validated by predicting the CS of 21 new concrete mixes from literature, resulting in prediction errors ranging from 0.5% to 9.9% and a R² of 0.93. The findings demonstrate that the proposed stacked regression approach significantly improves prediction accuracy and robustness compared to individual models, thereby facilitating more efficient and sustainable concrete mix design with reduced dependence on conventional experimental methods.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 11","pages":"4875 - 4895"},"PeriodicalIF":0.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strength and durability prediction of sustainable concrete incorporating biomedical waste ash and fly ash","authors":"Pranita S. Dambhare,&nbsp;Shrikrishna A. Dhale,&nbsp;Yashshree S. Dhale","doi":"10.1007/s42107-025-01457-x","DOIUrl":"10.1007/s42107-025-01457-x","url":null,"abstract":"<div><p>There has been an increased push in the research literature pushing the need for sustainable, high-performing concrete. Studies on partial cement replacement with industrial by-products, such as fly ash (FA) and biomedical waste ash (BMWA), have gained traction. However, the models traditionally used to estimate the compressive strength and durability of such concrete systems lag behind in the ability to capture intricate multi-scale interactions when associated with hybrid binder systems. Most of these predictive models are empirical or purely data-driven by neglecting or failing to rely on physical principles and generalization with varying curing conditions and material synergies. This would be addressed by this study with a fully derived ensemble proposing a new framework that incorporates the physics informed and data-driven technique for the prediction and optimization for M25-grade concrete mixes containing FA and BMWA in compressive strength (at 7, 14, 28 and 90 days) and durability prediction. The main psychic module called by this predictive framework is PINN-CSM (Physics Informed Neural Network for Concrete Strength Modeling); it puts physical laws such as Abram’s water-cement relationship and pozzolanic bounds into the loss function of the neural network, improving extrapolative performance and interpretability, achieving an R² ~ 0.96–0.98. Optimization is managed by MODE-STR (Multi-Objective Design Engine for Strength-Tradeoffs and Replacement) using NSGA II for optimal combinations of binders that balance strength, cost, and carbon impact sets. To further strengthen constituent interaction modeling, GNN-CS (Graph Neural Network for Constituent Synergy) leverages a graph-based message passing architecture to capture cross-material synergies while SHAP-FS (Shapley Aware Feature Synthesis) builds interpretable hybrid features with high joint influence on strength sets. It does so by aligning pre-trained models with experimental domains through transfer learning and hydration kinetics ontologies, TLO-CS (Transfer Learning with Ontological Fine-Tuning). This paper also used Random Forest (RF) and Gene Expression Programming (GEP) for evaluating the results under different scenarios. Thus, this integrated framework provides better accuracy and empowers the informed mix design towards low-carbon durable concrete optimized over multiple performance metrics.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 11","pages":"4811 - 4824"},"PeriodicalIF":0.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization-based evaluation of green rating frameworks for pre-engineered steel buildings using hybrid NSGA-III–MOPSO","authors":"Shailendra Kumar Khare,&nbsp;Anjali Gupta,&nbsp;Devendra Vashist","doi":"10.1007/s42107-025-01452-2","DOIUrl":"10.1007/s42107-025-01452-2","url":null,"abstract":"<div><p>Pre-engineered steel buildings (PESBs) are increasingly adopted for industrial applications due to their cost efficiency and rapid deployment. However, ensuring sustainability in PESBs requires a balanced evaluation of economic, environmental, and certification-related goals. This study develops a hybrid multi-objective optimization framework that combines the non-dominated sorting genetic algorithm III (NSGA-III) with multi-objective particle swarm optimization (MOPSO) to simultaneously optimize life cycle cost, embodied carbon emissions, green framework compliance scores, and construction time. A case study of an industrial warehouse in Hyderabad, India, is used to demonstrate the framework, incorporating green building standards such as LEED, IGBC, and GRIHA. The optimization explores alternative design configurations involving material selection, insulation thickness, sheeting type, and bracing systems. The resulting Pareto-optimal solutions highlight trade-offs among key performance metrics, enabling informed decision-making for stakeholders. Sensitivity analysis under varied stakeholder preferences further supports targeted design strategies. Comparative evaluation with other optimization techniques confirms the superiority of the proposed hybrid approach in convergence quality and solution diversity. This study offers a practical decision-support tool for sustainable PESB design, aligning industry practices with climate goals and certification requirements.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 11","pages":"4719 - 4738"},"PeriodicalIF":0.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating WSN and IoT for enhanced structural health monitoring in real-time using neural networks: a novel approach","authors":"Siraj Qays Mahdi,&nbsp;Sadik Kamel Gharghan,&nbsp;Hayder Amer Al-Baghdadi,&nbsp;Ammar Hussein Mutlag","doi":"10.1007/s42107-025-01459-9","DOIUrl":"10.1007/s42107-025-01459-9","url":null,"abstract":"<div><p>Structural health monitoring (SHM) of buildings is critically important as it directly affects human safety and economic activities. This paper proposes a real-time system design for SHM to monitor the building’s status through WSNs, make intelligent inferences, and predict the risk. The system architecture consists of multiple stages. The first stage is the transmitting side attached directly to the building’s structure, which comprises several sensors, including ADXL345, SW-420, LVDT, and strain gauge. LoRa wireless communication technology is established to transfer the sensors’ data from the transmitter side to an on-site central node. The central node processes and transmits the data to the cloud via Wi-Fi. The artificial neural networks (ANN) algorithm is employed to classify healthy and abnormal data to determine the damage severity value of the building’s status based on the peak ground acceleration (PGA), which ensures high accuracy in determining the damage value exposed to the building. The system utilizes the ThingSpeak IoT platform, which integrates the trained neural network and central node for storing sensors’ data and damage severity value to enable real-time monitoring. The system was validated using a shake table experiment by applying three PGA values, 0.05 g, 0.15 g, and 0.32 g, to the building model. The results demonstrate that the system is reliable and more effective for damage prediction, achieving a mean absolute error (MAE) of 0.0126 and 0.014 for neural network training and testing, respectively. Moreover, the ANN performed a correlation coefficient (R²) of 0.95892 and 0.95961 for training and testing. The main achievement of this research involves developing an advanced integrated system that combines sensors with an IoT platform and neural networks to track building damage severity in real-time.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 11","pages":"4839 - 4858"},"PeriodicalIF":0.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42107-025-01459-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184119","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":"Deep learning-based structural health monitoring of an ASCE benchmark building using simulated data","authors":"Maloth Naresh,&nbsp;Maloth Ramesh,&nbsp;Vimal Kumar,&nbsp;Joy Pal,&nbsp;Ashish B. Jadhav,&nbsp;Amruta D. Ware,&nbsp;Pranoti O. Shirole,&nbsp;Susmita A. Patil,&nbsp;Sudhakar S. Yadav,&nbsp;Abhijeet A. Hosurkar","doi":"10.1007/s42107-025-01462-0","DOIUrl":"10.1007/s42107-025-01462-0","url":null,"abstract":"<div><p>Structural health monitoring (SHM) is essential for ensuring the safety and functionality of civil infrastructure. This study presents a deep learning-based approach to SHM in the ASCE benchmark building. To achieve this, the ASCE benchmark building is modelled in the ANSYS environment to simulate its response under various structural conditions, including both undamaged and multiple damaged states. The acceleration data obtained from these simulations is converted into scalogram images using the continuous wavelet transform. These images are employed to train two deep learning algorithms for structural state classification: the Convolutional Neural Network (CNN) and the Alex Net algorithms. Compared to Alex Net, the CNN algorithm excelled at detecting subtle damage patterns. Additionally, MobileNetV2 is employed to evaluate performance under limited data conditions, achieving better classification accuracy. This approach offers a valuable and automated tool for real-time damage identification and decision-making in SHM applications.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 11","pages":"4897 - 4909"},"PeriodicalIF":0.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}