Asian Journal of Civil Engineering最新文献

{"title":"Integrated hybrid NSGA-III–MOPSO model for sustainable time–cost optimization in residential building construction in Nepal","authors":"Subash Kumar Bhattarai,&nbsp;Nischal Silwal,&nbsp;Mahesh Sharma,&nbsp;Asim Lohani,&nbsp;Mukesh Kafle","doi":"10.1007/s42107-026-01682-y","DOIUrl":"10.1007/s42107-026-01682-y","url":null,"abstract":"<div><p>This study presents a hybrid metaheuristic framework integrating Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-objective Particle Swarm Optimization (MOPSO) to solve the Time–Cost–Environmental Sustainability Trade-off (TCEST) problem for sustainable construction planning in Nepal. The model simultaneously minimizes project completion time and cost while maximizing environmental performance, tailored to the Nepalese construction context. A detailed case study of a G + 1 residential building in the Kathmandu Valley—comprising 21 activities with multiple execution modes—validates the framework. The hybrid algorithm generates a well-distributed Pareto front, offering diverse trade-off solutions relevant to Nepal’s unique cost structures, resource availability, and environmental priorities, such as reducing reliance on imported materials and minimizing riverbed material extraction. Performance metrics confirm the framework’s superiority over standalone algorithms. The Weighted Sum Method is applied to identify the most suitable solution based on stakeholder priorities. Correlation and trade-off analysis further reveal key interdependencies among objectives. Validation results indicate high predictive accuracy (R² &gt; 0.96) and Pareto front quality. Overall, the hybrid NSGA-III–MOPSO framework provides a robust, data-driven decision support system for Nepalese stakeholders to balance project timelines, budgets, and sustainability goals effectively.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3547 - 3564"},"PeriodicalIF":0.0,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386102","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":"Decision-making framework for sustainable concrete incorporating multiple waste-derived material replacements: an MCDM approach","authors":"Subhalaxmi Khumukcham,&nbsp;Khushpreet Singh,&nbsp;Mrinal Kanti Sen","doi":"10.1007/s42107-026-01716-5","DOIUrl":"10.1007/s42107-026-01716-5","url":null,"abstract":"<div><p>Sustainable alternatives that comprise lowering the consumption of resources without sacrificing structural performance. With the production of normal concrete, the environmental burden is increasing day by day. Sustainable M30-grade concrete can be developed by using various waste materials from industry, agriculture, and plastics. In this study, methodical experimental investigations are conducted on partial replacement of cement fine aggregate and coarse aggregate using a multi-criteria decision-making (MCDM) tool. MCDM is integrated for the simultaneous evaluation of compressive strength for the optimisation of the concrete mixes. Eight sustainable concrete models (M1 to M8) were developed in addition to a traditional control mix (M0). For the development of eight sustainable concrete models, a logical framework for material selection based on correlation analysis and the Best-Worst Method (BWM) for weightage determination has been used. Compressive strength testing has been used to evaluate the suggested mixes after 28 days, and unit weight and material cost were compared. The findings show that replacement systems based on minerals performed better than dominant mixes based on polymers and biomaterials. The Model M1 achieved the highest compressive strength, 41.48 N/mm², followed by M2 with 40.92 N/mm² and M3 with 39.43 N/mm²compressive strength. The models M1, M2, and M3 outperformed the control mix in compressive strength, weight reduction, and cost effectiveness. While Models M6-M8 demonstrated relatively lower strength and higher weight due to the extensive use of lightweight aggregates, Models M4 and M5 demonstrated intermediate performance because of the inclusion of polymeric and elastomeric materials. Models M1 to M3 are determined to be the most structurally and financially ideal sustainable mixes through a multi-criteria trade-off analysis that integrated strength, weight, and cost.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3893 - 3910"},"PeriodicalIF":0.0,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458679","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":"Explainable machine learning framework for predicting unconfined compressive strength of cement stabilized soils using SHAP and field validation","authors":"Debashish Chandra,&nbsp;Jasvir Singh","doi":"10.1007/s42107-026-01699-3","DOIUrl":"10.1007/s42107-026-01699-3","url":null,"abstract":"<div><p>Accurately predicting the unconfined compressive strength (UCS) of cement-stabilized soils is essential for designing safe and long-lasting geotechnical structures. Traditional empirical models often fall short in capturing the nonlinear interactions among geotechnical variables, cement content, and curing duration. This study introduces an explainable artificial intelligence (AI) framework for UCS prediction, utilizing seven advanced machine learning (ML) models: artificial neural network (ANN), support vector regression (SVR), decision tree regression (DTR), random forest regression (RFR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and CatBoost. A dataset of 500 samples with eight key input features—cement content, curing time, liquid limit, plasticity index, maximum dry density, optimum moisture content, fines content, and specific gravity—was used for model development. All models were evaluated using 10-fold cross-validation and multiple performance metrics, including R², RMSE, IOA, a20 accuracy, and prediction intervals. XGBoost achieved the best performance (R² = 0.923, RMSE = 0.269 MPa, IOA = 0.961, a20 = 94.8%). SHAP-based interpretability and OAT sensitivity analysis identified cement content and curing time as the most influential features. A field-representative case study and SHAP force plots further validated model robustness. The proposed framework offers accurate, transparent, and field-ready UCS prediction, supporting data-driven soil stabilization and geotechnical decision-making.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3869 - 3891"},"PeriodicalIF":0.0,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148459009","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":"Vibration response control of buildings using tuned inerter dampers","authors":"Pranav R. Sable,&nbsp;Ashish R. Akhare","doi":"10.1007/s42107-026-01695-7","DOIUrl":"10.1007/s42107-026-01695-7","url":null,"abstract":"<div><p>Multi-storey buildings often experience significant vibrations due to their flexible nature and exposure to earthquake and wind loads. These vibrations can affect structural safety and serviceability. Passive vibration control systems are commonly used due to their simple design and reliable performance. However, traditional tuned mass dampers have limitations related to the requirement of additional mass. Tuned inerter dampers provide an alternative solution by producing high inertial forces without adding much physical mass. This study examines how a nine degree of freedom shear building responds to vibrations when a tuned inerter damper is used. The building is subjected to base excitation, and its dynamic behaviour is examined through frequency-domain analysis. The damper is placed at the bottom and top storeys to study the effect of installation location. The influence of different inertance ratios on the structural response is also examined. The performance of the system is evaluated using normalized maximum displacement and inter-storey drift. The results show that placing the damper at the bottom storey gives a noticeable reduction in displacement and drift, even for small inertance ratios. On the other hand, when the damper is installed at the top storey, higher inertance values are required to obtain meaningful vibration reduction. The results show that the position of the damper and the inertance ratio are both important for controlling vibrations in a multi degree of freedom building.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3775 - 3793"},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458874","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":"Synergistic effects of DCFRP fibres, granulated blast furnace slag and oyster shell powder on the mechanical performance and failure behaviour of sustainable concrete","authors":"Maqsood Ali Malik,&nbsp;Wenying Xu,&nbsp;Umar Ayaz Lone","doi":"10.1007/s42107-026-01704-9","DOIUrl":"10.1007/s42107-026-01704-9","url":null,"abstract":"<div><p>This study systematically investigates the synergistic incorporation of discarded carbon fibre reinforced polymer (DCFRP) fibres, S105-grade granulated blast furnace slag (GBFS), and oyster shell powder (OSP) in C25 concrete. One control mixture and six modified mixtures were developed, incorporating a constant 1.0 vol% DCFRP fibre dosage, 5–10% GBFS as partial cement replacement, and 5–15% OSP as partial fine aggregate substitution. The fresh and hardened properties were evaluated through slump testing, 28-day compressive strength, axial compressive strength, splitting tensile strength, elastic modulus measurements, and failure mode assessment. 28-day microstructural characteristics were examined using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results demonstrate that moderate substitution levels produce pronounced mechanical enhancement within this multi-waste system. The optimal formulation (10% GBFS combined with 5–10% OSP) achieved a 22.4% increase in compressive strength and a 47.8% increase in splitting tensile strength relative to the control mix. Microstructural analyses indicate that these improvements are associated with secondary hydration induced by GBFS, improved particle packing from OSP, enhanced fibre–matrix interfacial bonding, and refinement of the interfacial transition zone (ITZ). In contrast, excessive OSP incorporation (15%) resulted in dilution effects and the persistence of unreacted calcite phases, leading to microstructural heterogeneity and reduced mechanical gains. These findings support the viability of carefully optimised multi-waste integration as a practical pathway for sustainable structural concrete production.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3829 - 3846"},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458933","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":"Compressive strength prediction of metakaolin mortar using CATBoost enhanced with genetic algorithm and particle swarm optimization","authors":"Poomporn Thamsatitdej,&nbsp;Divesh Ranjan Kumar,&nbsp;Warit Wipulanusat","doi":"10.1007/s42107-026-01705-8","DOIUrl":"10.1007/s42107-026-01705-8","url":null,"abstract":"<div><p>Metakaolin is a widely recognized supplementary cementitious material (SCM) among researchers and practitioners due to its ability to improve concrete qualities while reducing carbon emissions. The accurate estimation of the compressive strength (CS) of metakaolin mortar and finding the complex relationship among the mixture proportions and other variables is a difficult task for such a blended mixture. Conventional experimental methods are time-intensive and cost-intensive in nature, which limits the availability of comprehensive datasets. The main aim of the present study was to explore advanced machine learning techniques, namely, the CATBoost algorithm coupled with two metaheuristic optimization algorithms, the genetic algorithm (GA) and particle swarm optimization (PSO), for the prediction of CS of metakaolin mortar mix. For the construction of ML models, a dataset comprising 424 experimental records was collected from published literature, which included parameters such as cement grade, curing age, water-to-binder ratio, sand gradation, and plasticizer dosage. The performance of the base CATBoost model and its optimized variants (CATBoost-GA and CATBoost-PSO) was assessed using several performance indicators: R², WMAPE, NSE, RMSE, MAPE, and MAE. Among all developed models, CATBoost-PSO revealed the highest predictive performance on the test set (R² = 0.895, RMSE = 6.892 MPa, MAE = 4.421 MPa), outperforming both the CATBoost and CATBoost-GA models. The developed ML models had robust generalization capabilities in predicting the CS of MK mortar. However, the CATBoost-PSO model consistently produced the lowest prediction errors, showing its superior capacity to optimize base CATBoost model hyperparameters. The COM analysis results also demonstrate the resilience and versatility of ensemble learning methods, especially when integrated with optimization techniques. This study not only emphasizes the predictive efficacy of the base and hybrid CATBoost models for precise strength forecasting but also offers a user-friendly graphical user interface for predicting the CS of MK mortar based on the selected input variables of mix design factors.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3847 - 3868"},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458934","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":"Sustainable eco-mortar development using industrial by-products and recycled aggregates: an experimental and predictive study","authors":"G. Uday Kiran,&nbsp;G. Nakkeeran,&nbsp;P. Saravanakumar","doi":"10.1007/s42107-026-01691-x","DOIUrl":"10.1007/s42107-026-01691-x","url":null,"abstract":"<div>\u0000 \u0000 <p>This study evaluates the performance of sustainable mortar incorporating fly ash (FA) and limestone (LS) powder as partial binder replacements and construction and demolition waste (CDW) as an alternative fine aggregate. Cement was replaced by fly ash and limestone powder up to 24%, while natural sand was substituted with CDW up to 100%, maintaining a constant water-to-binder ratio of 0.45 and coconut fibre content of 0.3%. Experimental results indicate that the optimised mix containing 6% fly ash, 6% limestone powder, and 25% CDW exhibited superior performance. This mix achieved the highest dry density of 793.55 kg/m³ at 28 days, along with maximum ultrasonic pulse velocity, indicating improved internal compactness. Rebound hammer strength and compressive strength also peaked at this composition, confirming enhanced surface hardness and load-carrying capacity due to improved particle packing and pozzolanic activity. Beyond the optimum level, higher CDW content resulted in increased porosity and reduced mechanical performance. Machine learning models, particularly Gradient Boosting (GB) and Random Forest (RF), demonstrated excellent predictive accuracy with R² values exceeding 0.95 for all properties. The results confirm that controlled use of industrial and construction wastes can produce high-performance, low-carbon mortar systems.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3733 - 3746"},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458932","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 time–cost–environmental impact trade-off optimization using an opposition-based Rao-1 algorithm","authors":"Pokkuluri Kiran Sree,&nbsp;Miguel Villagómez Galindo,&nbsp;Ana Beatriz Martínez Valencia,&nbsp;Nageswara Rao Lakkimsetty,&nbsp;P. Lakshmi Prasanna,&nbsp;T. C. Manjunath","doi":"10.1007/s42107-026-01679-7","DOIUrl":"10.1007/s42107-026-01679-7","url":null,"abstract":"<div>\u0000 \u0000 <p>Time–cost–environmental impact trade-off problems (TCEITPs) are essential in sustainable construction project management, as decision-making must simultaneously address project duration, cost efficiency, and environmental performance. This study proposes a multi-objective optimization framework for solving TCEITPs using an opposition-based Rao-1 algorithm, in which opposition-based learning is incorporated to enhance population diversity and accelerate convergence toward well-distributed Pareto-optimal solutions. The effectiveness of the proposed approach is evaluated using a benchmark construction project comprising 25 activities with multiple execution alternatives. The obtained Pareto fronts are compared with those generated by the opposition-based multiple objective differential evolution (OMODE) method applied to the same case. Comparative results demonstrate that the proposed opposition-based Rao-1 algorithm is superior in terms of hypervolume (HV), spacing (SP), and the number of function evaluations required to reach high-quality Pareto solutions, indicating improved convergence efficiency and solution diversity. Overall, the findings confirm that the proposed framework provides a robust, efficient, and effective tool for solving complex TCEITPs, supporting informed decision-making in sustainable construction planning and contributing to the advancement of intelligent optimization techniques in project management.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3499 - 3510"},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386042","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":"Utilizing long short-term memory deep learning models to predict the compressive strength of geopolymer concrete","authors":"Konnoju Saikumar Chary,&nbsp;Vemu Venkata Praveen Kumar,&nbsp;Lingamallu Raghu Kumar,&nbsp;N. L. N. Kiran Kumar,&nbsp;Vasu Namala,&nbsp;G. Srikanth","doi":"10.1007/s42107-026-01700-z","DOIUrl":"10.1007/s42107-026-01700-z","url":null,"abstract":"<div><p>Geopolymer concrete (GPC) has emerged as a sustainable alternative to ordinary Portland cement due to its reduced carbon footprint and efficient utilization of industrial and waste-derived materials. However, predicting its compressive strength remains challenging because of complex nonlinear interactions among binder composition, alkaline activator chemistry, aggregate proportions, and curing conditions. This study proposes a Long Short-Term Memory (LSTM)-based deep learning model for accurate prediction of geopolymer concrete compressive strength. A comprehensive experimental dataset incorporating fly ash, eggshell powder replacement, SiO₂/Na₂O ratio, fine and coarse aggregate contents, reaction liquid dosage, and curing age was utilized. Exploratory data analysis, including histogram distributions, kernel density estimation, correlation matrix evaluation, and multivariate pair plots, revealed structured experimental variation and strong interdependency among mix parameters. The LSTM model was trained and validated using optimized hyperparameters, and its performance was evaluated through MSE, RMSE, and R² metrics. Training and validation loss curves demonstrated stable convergence without overfitting. The strong agreement between predicted and experimental values, along with normally distributed residuals centered around zero, confirms the robustness and generalization capability of the proposed framework. The study highlights the potential of deep learning techniques for intelligent, data-driven design and optimization of sustainable geopolymer concrete systems.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3795 - 3808"},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458875","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":"Seismic performance evaluation of SCBF systems designed under Iran’s seismic design provisions (standard 2800) using FEMA P695 methodology","authors":"Amirali Hadinejad,&nbsp;Abazar Asghari","doi":"10.1007/s42107-026-01687-7","DOIUrl":"10.1007/s42107-026-01687-7","url":null,"abstract":"<div>\u0000 \u0000 <p>The evolution of Iran’s Seismic Design Standard No. 2800, including the transition from the fourth to the draft fifth edition, has largely relied on linear analyses with limited nonlinear verification, leaving uncertainty regarding the effects of recent revisions on actual seismic performance. This issue is particularly relevant for Special Concentrically Braced Frames (SCBFs), which are widely used yet insufficiently evaluated within performance-based frameworks. To address this gap, this study presents a nonlinear assessment of SCBF systems designed according to the fourth and fifth editions of Standard 2800, focusing on three key revisions: the response modification factor (R), the approximate fundamental period coefficient, and the importance factor. Five representative SCBF archetypes, ranging from 2 to 14 stories, were designed in ETABS and modeled in OpenSees. Nonlinear static and dynamic analyses were performed under 44 far-fault and 50 near-fault ground motion records following the FEMA P695 methodology. Engineering demand parameters, including peak and residual interstory drift ratios, peak floor accelerations, deflection amplification ratios, and normalized story-shear participation, were evaluated, while cumulative and probability density functions quantified response variability. Results indicate that linear analyses predict a 7–13% reduction in design base shear for mid-rise and tall frames under the fifth edition; however, nonlinear responses show improved post-buckling stability and reduced mean PIDR and RIDR due to deformation redistribution rather than force reduction. Short-period frames exhibit amplified nonlinear drift under near-fault pulse-type motions, with Cd-normalized ratios exceeding unity, whereas longer-period frames show normalized interstory drift ratios of 0.8–0.9, indicating conservative deformation estimates. Far-fault records produce pronounced upper-story acceleration amplification, confirming that acceleration-controlled performance remains critical in tall SCBFs.</p>\u0000 </div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3629 - 3662"},"PeriodicalIF":0.0,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386048","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}