Asian Journal of Civil Engineering最新文献

{"title":"Prediction of compressive strength, static modulus and wenner resistivity for normal concrete using different percentages of recycled concrete as a coarse aggregate","authors":"Sheetal Thapa,&nbsp;Nagondanahalli Raju Asha Rani,&nbsp;Richi Prasad Sharma","doi":"10.1007/s42107-025-01303-0","DOIUrl":"10.1007/s42107-025-01303-0","url":null,"abstract":"<div><p>The two most important mechanical properties for concrete are compressive strength and static modulus. Likewise, Wenner resistivity is a crucial durability parameter to be taken into consideration while monitoring the performance of any concrete members. This paper presents novel prediction models for normal concrete’s compressive strength, static modulus, and Wenner resistivity based on linear regression models and artificial neural networks (ANN). Due to the quicker rate of output convergence, the study used the Levenberg–Marquardt learning algorithm for the ANN model to forecast the aforementioned parameters. The prediction strength (R2) of the ANN technique is 14–20% higher than that of the normal regression model, 11–14% higher than that of the static modulus model, and 10–12.5% higher than that of the Wenner resistivity model. For both ANN and linear regression models, the input parameters considered were the rebound number and pulse velocity. The sample was evaluated by substituting normal stone aggregate (NSA) with varying amounts of recycled concrete aggregate (i.e., 0%, 25%, 50%, 75%, and 100% RCA) as a coarse aggregate. This study considered age (14, 28, and 90 days) and grade (M20, M25, and M30) into consideration while developing the models. Furthermore, by comparing the developed compressive strength model with earlier models created by other authors, the study found that the generated model performed better for RCA specimens. The findings of this investigation will support the application of RCA in the Indian construction sector and promote utilization of natural coarse aggregate more sustainably.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 5","pages":"2135 - 2152"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888505","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":"Direction dependent interface shear resistance of structured surfaces in sandy soils under monotonic loading","authors":"Mu’ath I. Abu Qamar,&nbsp;Ammar A. Alshannaq,&nbsp;Mohammad F. Tamimi","doi":"10.1007/s42107-025-01317-8","DOIUrl":"10.1007/s42107-025-01317-8","url":null,"abstract":"<div><p>Foundation elements with load-carrying capacity rely on interface resistance between elements and surrounding soils, which could benefit from utilizing surfaces with structured roughness. The performance and efficiency of foundations’ supporting geo-structures where higher resistance is desired in one direction and lower resistance in the other direction is an advantage. This study focuses on evaluating the influence of structured roughness design on the interface resistance in sandy soils with different characteristics (mainly grain size). A conventional direct shear test apparatus was modified to experimentally evaluate the interface shear resistance of structured (rough-textured) and untextured (smooth) surfaces in two shearing directions. The interface shear tests were performed on locally available fine, mixed, and medium sands at the same shearing rate and over a range of normal confining stresses. The interface shear test results on smooth surface in the three studied sands showed that the difference in mobilized resistance in both directions (interface resistance anisotropy) is marginal. However, results from tests on structured (rough) surfaces suggest that the mobilized shear resistance is direction dependent (i.e., different) when surfaces with different number of elements were sheared against and along the fine, mixed, and medium sand specimens. The results also indicate that mixed sand showed higher anisotropy followed by the medium, while the interface resistance anisotropy was minimal in fine sand. It was also found that the normal confining stress applied at sand-structured (rough) surface interface has a crucial impact on mobilized resistance under monotonic axial loading.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2419 - 2432"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074166","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":"Influence of the size of the coarse and fine aggregates on the compressive strength of concrete","authors":"Pritam Dey,&nbsp;Sneha Singh,&nbsp;Ramagopal Uppaluri","doi":"10.1007/s42107-025-01297-9","DOIUrl":"10.1007/s42107-025-01297-9","url":null,"abstract":"<div><p>In this study, the influence of aggregate sizes on the compressive strength (CS) of concrete system has been assessed. Accordingly, the fine aggregates (FA) and coarse aggregates (CA) with fixed gradation sizes were considered and the prepared conventional concrete samples were assessed for their 7th and 28th day compressive strength. Thereby, three different types of FA were selected for FA particle size gradation. Striking variations in the CS values were noted for the concrete samples with a particular combination of FA and CA sizes, and for a fixed choice of the water-to-cement ratio. Further investigations for the CS modelling are conducted with the response surface methodology (RSM). Considering overall specific gravity (FA Sp.Gr.) as a parameter but not a factor, the experimental design considered the FA size (in µm) and CA size (in mm) as independent variables. The best-fit RSM model analysis inferred the quadratic model with good relevance (<i>p</i> &lt; 0.001) for the prediction CS in the defined factor space and for three alternate FA types. The Pareto plots revealed that while the FA size was the influential factor for both responses for the case of the FA Sp.Gr. value of 2.59, while CA size was the highest contributor for the other two FA Sp.Gr. types. Thereby, the role of the type of FA and the aggregate sizes were assessed to be very important to achieve the high CS. The adopted methodology is generic for selecting the best-fit control sample for further research into advanced concrete composite materials.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 5","pages":"2053 - 2070"},"PeriodicalIF":0.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888642","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":"Efficacy of image based deep learning CNN models for bolt loosening detection in steel structures","authors":"Mallika Alapati,&nbsp;G. Ramesh Chandra,&nbsp;Shivani Abboju","doi":"10.1007/s42107-025-01316-9","DOIUrl":"10.1007/s42107-025-01316-9","url":null,"abstract":"<div><p>Bolted connections are integral components in steel structures and often are vulnerable to loosening due to cyclic loading and fatigue. Detecting bolt loosening in early stages is critical to prevent sudden catastrophic failures. Recent advancements in image capturing and processing, combined with machine learning techniques have significantly improved the accuracy of detecting bolt loosening. Deep learning techniques, in specific those based on image analysis, have proven to be effective tool for damage detection in structural members. In this study, initially captured dataset of 120 images from a laboratory model is augmented to 576 images using the Roboflow platform. Investigations in this work are tied to one image data set and the metrics are analysed for how well a model performs. Usually, it depends on each use case as to which model would be best suited. In the present work, dataset featured two states of bolt loosening: tight and loose. Convolution Neural Network architectures AlexNet and ResNet are applied individually to the augmented dataset to predict the bolt loosening states as binary classification problem. The results demonstrated an accuracy of 87.931%, indicating that the AlexNet model effectively distinguished between loose and tight conditions. Whereas the ResNet algorithm exhibited relatively better accuracy of 90.8%. Further, an integrated hybrid model (CNN model with LSTM) is also applied on the same image data to further enhance the efficiency of the deep learning frame work. The performance of the trained CNN algorithms was further validated with image responses from various real time scenarios of various lighting conditions and found to be fairly good. This study highlights the potential of using deep learning techniques, specifically AlexNet, ResNet and CNN with LSTM for accurately detecting bolt loosening, thereby contributing to the safety and reliability of steel structures.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2409 - 2417"},"PeriodicalIF":0.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074208","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":"Soft computing-based prediction of recycled aggregate concrete strength: impact of aggregate properties and mix design parameters","authors":"Sewa Soran Othman,&nbsp;Zanyar Qadir Ahmed,&nbsp;Ahmed Salih Mohammed,&nbsp;A. M. T. Hassan","doi":"10.1007/s42107-025-01312-z","DOIUrl":"10.1007/s42107-025-01312-z","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Ordinary concrete, comprising 80% aggregate and 12% cement by mass, is widely utilized but contributes considerably to environmental issues, including waste production, carbon emissions, and resource depletion. Recycled aggregate concrete (RAC) offers a sustainable alternative by reusing concrete waste, reducing the demand for natural aggregates, and reducing the environmental damage caused by their extraction. Among its properties, compressive strength (CS) is a crucial mechanical indicator of RAC's structural performance, as it establishes the material's ability to support applied loads and adapt to various building applications. This makes evaluating RAC’s potential as a sustainable building material critical. The classical Experimental approaches for predicting compressive strength are time-intensive and pricey. To build analytical models, 18 inputs and 289 data samples were gathered from previous literature. The independent variables are cement, 101 to 520 kg/m&lt;sup&gt;3&lt;/sup&gt;; fly ash, 0 to 390 kg/m&lt;sup&gt;3&lt;/sup&gt;; slags, 0 to 195 kg/m&lt;sup&gt;3&lt;/sup&gt;; natural coarse aggregate, 0 to 1150 kg/m&lt;sup&gt;3&lt;/sup&gt;; recycled coarse aggregate, 0 to 1040 kg/m&lt;sup&gt;3&lt;/sup&gt;; the replacement rate of recycled coarse aggregate, 0 to 100% with other variables that include silica fume, sand, water/binder, the apparent density of natural coarse aggregate, the apparent density of recycled coarse aggregate, water absorption of natural aggregate, water absorption of recycled aggregate, the maximum particle size of recycled coarse aggregate, cement type, and fineness modulus of recycled coarse aggregate. The compressive strength of this study ranged from 2.44 to 83.70 MPa. Five modeling methods, including Linear Regression, Multi-Linear Regression, Pure Quadratic, Non-Linear Regression, and Non-Linear Inverse Regression, have been conducted in this study to predict the compressive strength of recycled aggregate concrete. With superior outcomes across all evaluation criteria, the Non-Linear Regression (NLR) model was the most effective predictor of compressive strength. Nevertheless, the Multi-Linear Regression (MLR) model also performed well. Residual error analysis confirmed that the NLR model revealed the least error compared to the other models. Sensitivity investigation shows that age, cement content, and water significantly affect the compressive strength of recycled aggregate concrete. The findings of this study highlight that the compressive strength of natural coarse aggregate is higher than that of recycled coarse aggregate, and to increase the compressive strength of recycled aggregate concrete, the mortar attached to the recycled coarse aggregate should be removed. RAC is an efficient alternative for environmentally friendly buildings as it demonstrates enough strength for application on roads, pavements, retaining walls, and temporary structures. The results highlight how RAC may solve environmental issues without sacrificing structural integrity, opening t","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2349 - 2369"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074221","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 modelling for strength prediction and durability investigation of alkali activated binders with POFA and granite dust","authors":"Mehar Sai Komaragiri,&nbsp;Subhani Shaik,&nbsp;Santhosh Kumar Gedela,&nbsp;Sk M. Subhani","doi":"10.1007/s42107-025-01319-6","DOIUrl":"10.1007/s42107-025-01319-6","url":null,"abstract":"<div><p>The rapid growth of urbanization and the construction industry has led to increased consumption of natural resources, resulting in significant environmental impacts. This study explores the use of three locally available waste materials to develop sodium- and potassium-based alkali-activated binders. Granite dust was employed as an alternative to river sand, with replacement levels ranging from 0 to 50%, and optimized for performance. Additionally, palm oil fuel ash (POFA) was utilized as a source material, replacing slag at levels of 10% to 30% in a control mix, activated using NaOH &amp; Na₂SiO₃ and KOH &amp; K₂SiO₃ under both heat curing at 65 °C and ambient curing conditions. The mechanical and durability properties like compressive strength, water absorption, sorptivity and resistance to acids with influence of the activator, and microstructural characteristics of the binders were thoroughly analyzed. The temperatures effects of specimens were clearly analyzed and the heat cured specimens gives the 25% of lesser strength than the ambient cured AAB irrespective of activator. In both sodium and potassium based alkali activated binders. K-Nearest Neighbors and artificial neural networks were used to forecast the alkali-activated mortar’s compressive strength. Metrics used for performance evaluation, such as the coefficient of determination R² and RMSE, showed that the ANN model produced better predictions. For sodium-based activators, ANN produced an RMSE of 0.174 and an R² value of 0.96 under ambient curing conditions, while KNN produced an RMSE of 0.154 and an R² value of 0.158. The findings highlight the potential use of waste materials, such as POFA, granite dust and slag in the creation of eco-friendly and high-performance alkali-activated binders.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 6","pages":"2447 - 2464"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144074220","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":"Boosting multi-objective aquila optimizer with opposition-based learning for large-scale time–cost trade-off problems","authors":"Yusuf Baltaci","doi":"10.1007/s42107-025-01306-x","DOIUrl":"10.1007/s42107-025-01306-x","url":null,"abstract":"<div><p>This study presents an enhanced version of the Aquila optimizer (AO), known as the opposition-based aquila optimizer (OBAO), which incorporates opposition-based learning (OBL) to enhance performance. By considering both current solutions and their opposites, OBL expands the search space, increasing the chances of avoiding local optima and identifying superior solutions. Additionally, OBL replaces the expanded and narrowed exploitation methods of the original AO, reducing computational complexity and enhancing the efficiency of the proposed model. The proposed OBAO is applied to a large-scale time–cost trade-off problems (TCTP) with 630 activities, demonstrating its capability to efficiently achieve optimal or near-optimal solutions. Comparative assessments against advanced optimization algorithms, including teaching learning-based optimization (TLBO), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and plain AO indicate that OBAO achieves better solutions in terms of number of objective function evaluations (NFE) and hypervolume (HV) indicator. The findings suggest that OBAO is a promising alternative for optimizing large-scale construction projects in construction management field.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 5","pages":"2179 - 2188"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888744","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":"Flexural behavior of composite beam with different shear connectors","authors":"Mohammad Osman Ghone,&nbsp;Md. Khasro Miah,&nbsp;Md. Rakibul Hasan,&nbsp;Noor Md. Sadiqul Hasan,&nbsp;Md Jihad Miah","doi":"10.1007/s42107-025-01274-2","DOIUrl":"10.1007/s42107-025-01274-2","url":null,"abstract":"<div><p>This study aims to evaluate the flexural performance, crack resistance, and mechanical properties of composite beams. It specifically focuses on the performance characteristics of locally available inverted L-type shear connectors, headed shear connectors, and concrete made with brick aggregates and stone aggregates. This study examined how headed and inverted L-type shear connectors affect the steel–concrete composite beam’s flexural and mechanical performance. Thus, the experiment maintained the concrete mix design and reinforcement ratio while varying headed and inverted L-type connectors. The impact of shear connections improves the structural performance of composite beams compared to non-composite beams without them. The ultimate load capacity, corresponding deflection, and midspan deflection curve were examined in relation to the experimental data. The test specimens' failure behaviour was also investigated. The concrete slab’s flexural shear cracking and the steel flange's local buckling caused the composite beams to fail. The CBHS specimen improves in load-bearing (46.84%) and deflection reduction (61.30%), while CBIB demonstrates consistency, CBIS variability, and moment capacity increases are minor, demonstrating CBHS's efficiency and optimization possibilities. The test results show specimens with headed shear connectors function better than those with inverted L-type shear connectors.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 5","pages":"1919 - 1938"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888741","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":"Prioritizing passive envelope design features for integration into the building energy codes: a case of India","authors":"Kuladeep Kumar Sadevi,&nbsp;Avlokita Agrawal","doi":"10.1007/s42107-025-01275-1","DOIUrl":"10.1007/s42107-025-01275-1","url":null,"abstract":"<div><p>Energy efficiency in buildings is a key area of focus in the path towards net zero energy goals and mitigating climate change. Among various passive strategies for energy efficiency in buildings, building envelope shading is considered a key strategy to control solar heat gain and reduce the cooling loads in buildings. While significant focus has been given to shading glazing components of buildings, this paper addresses a critical gap by investigating the potential of shading the opaque envelope components (OECs), which include opaque walls and roofs. OECs can reduce cooling load by managing the heat ingress into the buildings. Although OECs are not widely used in energy-efficient building strategies, the passive strategies in OECS help control heat gains and decrease cooling loads, especially in hot climates. This study investigates the shading strategies of opaque OECs in a two-stage review, initially reviewing the global building energy codes to assess the inclusion of OEC shading strategies and then identifying effective shading techniques for walls and roofs through a systematic literature review. The review of energy codes reveals that very few energy codes explicitly address OEC shading with a single instance mentioned in the energy codes of Australia and India. In contrast, all the codes explicitly specify window shading as a passive strategy. The SLR further demonstrates that the shading OECs can significantly reduce cooling demands, with strategies such as overhangs, green facades, double-skin roofs, and photovoltaic panels showing up to 77% energy savings. The shading potential provides considerable scope for integrating the shading of OECs as a passive strategy that can be incorporated into the energy for better adoption in the buildings.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 5","pages":"1865 - 1879"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888739","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 development factors in building engineering systems: do demographic factors matter?","authors":"Priji Biju,&nbsp;Nahia Mourad,&nbsp;Ahmed Mohamed Habib","doi":"10.1007/s42107-025-01301-2","DOIUrl":"10.1007/s42107-025-01301-2","url":null,"abstract":"<div><p>Sustainable engineering builds systems, products, and processes that are socially, environmentally, and economically viable to fulfil the promise of a balanced approach to achieve the net zero emission targets of the world to mitigate climate change impacts. Owing to the multidisciplinary nature of sustainable development, sustainability efforts involve concepts, principles, and methods from engineering, social sciences, economics, social psychology, biological sciences, ecology, and physical sciences. Hence, scientific analysis is required to define inter-item relationships and identify differences based on the demographic features of professionals. The lack of such studies in the literature represents the main gap covered by this study. In this context, a methodology was designed and applied by surveying 101 professionals from various engineering disciplines in the UAE’s construction sector. The results confirmed a significant correlation among sociocultural (SOC), economic (ECO), and environmental (ENV) sustainability factors. The findings revealed that the distributions of SOC, ECO, and ENV were the same across gender, specialisation, and experience categories. Moreover, the distributions of ECO and ENV were the same across age categories, except for the distribution of SOC, which differed across age categories, favouring groups over 25 years of age. These findings would support stakeholders in the construction sector in developing sustainable engineering building systems. The proposed methodology can be used in other areas to help stakeholders establish sustainable systems based on the SOC, ECO, and ENV factors.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"26 5","pages":"2101 - 2116"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888740","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}