Asian Journal of Civil Engineering最新文献

{"title":"An integrated optimization framework for sustainable pre-engineered steel buildings: coupling NSGA-III–MOPSO with green building rating systems","authors":"Esar Ahmad,&nbsp;P. D. Maneeth,&nbsp;Shashank Gupta,&nbsp;Shyamveer Singh Chauhan,&nbsp;Nageswara Rao Lakkimsetty,&nbsp;G. Pavithra","doi":"10.1007/s42107-026-01721-8","DOIUrl":"10.1007/s42107-026-01721-8","url":null,"abstract":"<div><p>Pre-engineered steel buildings (PESBs) have emerged as a fast and cost-effective construction solution for industrial infrastructure; however, their sustainability performance remains inadequately addressed due to high embodied carbon and limited integration with green rating systems. This study proposes a hybrid multi-objective optimization framework combining Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-Objective Particle Swarm Optimization (MOPSO) to evaluate and optimize PESB designs across economic, environmental, and regulatory dimensions. Four conflicting objectives—life cycle cost (LCC), embodied carbon emissions (ECE), green framework compliance score (GCS), and construction time (CT)—are simultaneously optimized. A real-world industrial warehouse case study in Hyderabad, India is analyzed under LEED, IGBC, and GRIHA rating frameworks. The hybrid algorithm leverages the diversity preservation capability of NSGA-III and the fast convergence of MOPSO to generate well-distributed Pareto-optimal solutions. Results demonstrate significant trade-offs among objectives, with high-compliance designs incurring higher costs and moderate carbon reductions, while cost-optimal solutions exhibit lower sustainability performance. The proposed framework outperforms conventional optimization techniques in terms of convergence, diversity, and solution quality. Scenario-based decision analysis further highlights the adaptability of the model to stakeholder-specific priorities. The study provides a robust decision-support tool for sustainable PESB design and contributes to advancing optimization-driven green building assessment in industrial construction.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"4075 - 4093"},"PeriodicalIF":0.0,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458999","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":"Predictive modelling of fibre-reinforced geopolymer concrete strength using ensemble learning techniques","authors":"Debidatta Behuria,&nbsp; Debasree,&nbsp;Nihar Ranjan Mohanta,&nbsp;Tapas Mohanty,&nbsp;Trilochan Sahu","doi":"10.1007/s42107-026-01714-7","DOIUrl":"10.1007/s42107-026-01714-7","url":null,"abstract":"<div><p>Fibre reinforcement in GPC significantly contributes to its ability to resist deformation and inhibit the propagation of cracks during tensile and flexural loading conditions. However, how the compressive strengths of fibre-reinforced geopolymer concrete (FRGPC), with steel hook fibres, can be predicted has been difficult due to the intricate relationship of the various components of the concrete mix that require adequate consideration during the process. Although artificial intelligence/machine learning (ML) has been indicated by various researchers as an alternative in modelling geopolymer concrete data, its use in FRGPC data has been on the periphery of research-based data due to the intricate mechanisms of concrete formation. In this study, an attempt has been made to produce an optimal data set using the Taguchi orthogonal array. In this regard, the proposed framework has produced a data set with 110 data points, covering all the necessary input parameters consisting of 16 input variables with varying percentages of GGBS and fly ash in the concrete mix. Considering the above results obtained with the Taguchi approach, an implementation of Linear Regression (LR), Decision Tree (DT), and Random Forest (RF) models was carried out with regards to compressive strength predictions. To evaluate the accuracy of the models, a set of statistical measures were used, including mean absolute error, mean squared error, root mean square error, mean absolute percentage error, and coefficient of determination, with k-fold cross-validation used in the models to avoid overfitting. Based on the results, a high predictive efficiency was recorded regarding all models used, while the Random Forest algorithm recorded higher accuracy with an R² of 0.906 and an RMSE of 3.822. The most important variables that affect compressive strength were identified in the sensitivity analysis as age of the specimens, alkaline solution to GGBS mass ratio, and dosage of sodium hydroxide. Equally important was the finding that Taguchi and ML were an efficient framework in the prediction of FRGPC compressive strengths.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"4055 - 4073"},"PeriodicalIF":0.0,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458997","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":"Data-driven prediction and optimization of unconfined compressive strength in lime-stabilized soils using explainable machine learning","authors":"Prashant Tiwari,&nbsp;Smita Tung","doi":"10.1007/s42107-026-01708-5","DOIUrl":"10.1007/s42107-026-01708-5","url":null,"abstract":"<div><p>This study presents a hybrid experimental–machine learning–optimization framework for predicting the unconfined compressive strength (UCS) of lime-stabilized subgrade soils. Laboratory experiments were conducted on low-plasticity clay with varying lime content (0–10%) and curing periods, generating a dataset that was further expanded and statistically validated. Multiple machine learning models, including MLR, SVR, ANN, DTR, RFR, XGBoost, and CatBoost, were developed and evaluated using performance metrics such as R², RMSE, MAE, IOA, and VAF. Among these, CatBoost achieved the highest accuracy (R² = 0.989, RMSE = 39.85 kPa), demonstrating superior capability in capturing nonlinear soil behavior. Explainable AI techniques, including SHAP and partial dependence plots, were applied to interpret predictions, revealing lime content and maximum dry density as dominant positive factors, while plasticity index showed a negative influence. A multi-objective optimization framework using NSGA-II was implemented to maximize UCS while minimizing lime content and plasticity. Pareto-optimal solutions were ranked using TOPSIS, identifying optimal mix designs for practical applications. The proposed framework provides a reliable, interpretable, and efficient approach for sustainable soil stabilization and engineering decision-making by integrating experimental insights with advanced data-driven modeling and optimization strategies to enhance performance, reduce material usage, and support cost-effective infrastructure development in geotechnical engineering practice contexts.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"4013 - 4034"},"PeriodicalIF":0.0,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458862","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-driven prediction of mechanical properties in fly ash-based geopolymer concrete incorporating silica fume and steel slag","authors":"S. R. Hemalatha,&nbsp;M. Sreedevi,&nbsp;Bhukya Govardhan Naik,&nbsp;Nakkeeran Ganasen,&nbsp;R. Nidhya","doi":"10.1007/s42107-026-01707-6","DOIUrl":"10.1007/s42107-026-01707-6","url":null,"abstract":"<div><p>This study explores fly ash-involved geopolymer concrete (GPC) using silica fume (SF) (0–60 kg/m³) and steel slag (SS) (0–720 kg/m³) as additional industrial byproducts to assess mechanical behavior. Sixteen mix proportions were systematically designed by varying fly ash (340–400 kg/m³), SF, and SS as partial replacements for natural coarse aggregate, activated using a constant 12 M NaOH and Na₂SiO₃ alkaline solution. Experimental evaluation of hardened specimens was done on bulk density, compressive strength (CS), split tensile strength (TS), flexural strength (FS), and ultrasonic pulse velocity (UPV) at 7 days and 28 days of ambient curing. A mix of 7 with 5% SF and 40% SS replacement gave the optimum mechanical performance with a 28-day CS of 44 MPa, TS of 4.6 MPa, FS of 7.0 MPa, and UPV of 4.9 km/s, which meets M40 grade requirements as per IS 456:2000. The Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms were used to estimate the output properties, with KNN proving to be more accurate at all the parameters (R² = 0.949, RMSE = 8.57 MPa in the case of CS). The most significant predictors were observed to be SS and natural coarse aggregate (feature importance analysis). The proposed ML framework reduces experimental dependency by approximately 80%, offering an efficient and sustainable approach to GPC mix design optimization.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3997 - 4011"},"PeriodicalIF":0.0,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458861","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":"Physics-informed neural networks for quantitative corrosion identification in steel I-beams: a robust inverse mechanics framework","authors":"Duy-Duan Nguyen,&nbsp;Viet-Chuong Ho,&nbsp;Xuan-Hung Vu,&nbsp;Thanh-Tung Nguyen Thi,&nbsp;Trong-Ha Nguyen","doi":"10.1007/s42107-026-01673-z","DOIUrl":"10.1007/s42107-026-01673-z","url":null,"abstract":"<div><p>Metal corrosion is a major degradation mechanism that reduces the load-carrying capacity and serviceability of steel structural members. Quantitative identification of corrosion-induced stiffness loss from limited structural response measurements remains a challenging inverse problem, particularly under sparse sensing and measurement noise. This study presents a physics-informed neural network (PINN)-based framework for identifying spatially varying flexural rigidity in steel I-beams. The proposed approach embeds the Euler–Bernoulli beam equation directly into the loss function, enabling simultaneous inference of the transverse displacement field and the stiffness degradation field from sparse displacement data. The framework is evaluated through numerical case studies involving uniform, localized, and multi-point corrosion scenarios. Gaussian noise levels of up to 5% are considered to examine robustness under measurement uncertainty. The results indicate that the proposed method can identify the location and magnitude of stiffness degradation with low estimation error under the investigated conditions. Comparisons with a conventional finite element model updating approach and a purely data-driven deep neural network model show improved identification accuracy and localization capability for the examined benchmark problem. The findings suggest that embedding governing mechanical equations into neural network training enhances solution stability and data efficiency in inverse stiffness identification problems. Nevertheless, the present validation is limited to numerical simulations, and further experimental investigation is required to assess field applicability.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 7","pages":"3399 - 3412"},"PeriodicalIF":0.0,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386099","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":"Interpretable hybrid metaheuristic-optimized Gaussian process regression for predicting ultra-high-performance concrete compressive strength","authors":"Amrendra Bharti,&nbsp;Sapan Kumar,&nbsp;Nishant Kumar,&nbsp;Meyarul Islam,&nbsp;Sujeet Kumar","doi":"10.1007/s42107-026-01696-6","DOIUrl":"10.1007/s42107-026-01696-6","url":null,"abstract":"<div><p>Ultra-high-performance concrete (UHPC) has emerged as an advanced construction material due to its superior strength, durability, and resistance to aggressive environmental conditions. However, predicting its compressive strength is complex because UHPC properties depend on multiple interacting material and workability parameters. Therefore, the present study aims to develop an efficient machine learning-based predictive framework for estimating the compressive strength of UHPC. A comprehensive database consisting of 360 experimental records was compiled from previous studies. Eight key input parameters, fly ash, silica fume, T500 flow time, maximum spread diameter, J-ring value, h2/h1 ratio, V-funnel time, and temperature were considered as predictors. Two hybrid machine learning models were developed using Gaussian Process Regression (GPR) optimized with metaheuristic algorithms, namely the Snow Geese Algorithm (SGA) and Horse Herd Optimization (HHO). The dataset was normalized and divided into training, validation, and testing subsets. Model performance was evaluated using several statistical indicators including root mean square error, mean absolute error, correlation coefficient, weighted mean percentage absolute error, and variance accounted for. The results demonstrate that both hybrid models provide reliable predictions of UHPC compressive strength, while the GPR–SGA model achieved slightly higher accuracy than the GPR–HHO model. Furthermore, explainable artificial intelligence techniques such as SHAP analysis and Partial Dependence Plots were applied to interpret the influence of input variables. The analysis revealed that temperature and silica fume content have the most significant impact on compressive strength prediction. The proposed modeling framework provides a practical decision-support tool for engineers to estimate UHPC strength efficiently, optimize mixture design, and reduce the need for extensive laboratory experimentation.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3973 - 3995"},"PeriodicalIF":0.0,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458782","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":"Reliability analysis of components of bridge beam structure","authors":"Abhishek Kumar Singh,&nbsp;Pijush Samui","doi":"10.1007/s42107-026-01706-7","DOIUrl":"10.1007/s42107-026-01706-7","url":null,"abstract":"<div><p>The study integrates the First-Order Reliability Method (FORM) with a K-Nearest Neighbours (KNN) regression model to estimate reliability indices of bridge deep-beam components. The novelty lies in developing a data-driven reliability evaluation framework that compares analytical and machine-learning-based reliability indices. The main aim of this paper is to analyse the reliability of deep beams using both traditional probabilistic methods and machine learning approaches. The study addresses key challenges by evaluating the performance and safety of structural elements under varying loads and material conditions. We created 250 datasets by varying the live load and the concrete’s characteristic compressive strength, fck. Shear stress and deflection, the main outputs, were calculated using structural design principles. We first evaluated the reliability of deep beams using the First-Order Reliability Method on a real dataset. The reliability index (β) values were 1.50 for deflection and 13.61 for shear stress, showing strong structural safety, especially for shear that suggests a serviceability limit state concern. Next, a KNN model was developed to predict deflection and shear stress. The KNN model demonstrated high predictive power, with R² values of 0.998 and 0.996, and Mean Squared Error values of 0.00001 (training) and 0.0003 (testing). Applying FORM to the KNN-predicted outputs yielded β values of 13.61 for shear stress and 1.51 for deflection. The small difference between reliability indices and failure probabilities in actual and predicted datasets validates the effectiveness of the KNN model. Results indicate close agreement between analytical and predicted outcomes, demonstrating the applicability of ML-assisted reliability analysis. The findings demonstrate that the KNN algorithm is highly effective for reliability analysis and prediction of structural responses. Integrating machine learning with traditional probabilistic methods provides a strong framework for assessing safety and performance in complex structural systems such as deep beams. The study also highlights computational efficiency and practical limitations of the proposed approach.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3961 - 3971"},"PeriodicalIF":0.0,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458799","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":"Experimental and machine learning investigation of hybrid FRP strengthened high strength concrete","authors":"P. Sowmiyadevi,&nbsp;R. Arvind Saravan,&nbsp;C Vinothini,&nbsp;G. Nakkeeran,&nbsp;T. Subbulakshmi","doi":"10.1007/s42107-026-01697-5","DOIUrl":"10.1007/s42107-026-01697-5","url":null,"abstract":"<div><p>This study investigates the mechanical performance of high-strength concrete (HSC) incorporating silica fume (SF) as a supplementary cementitious material and waste glass aggregate (WGA) as a natural coarse aggregate replacement, with potential application in hybrid FRP-strengthened systems. Nine concrete mixtures were prepared with SF (0–20%) and WGA (0–100%) at a constant water-to-binder ratio of 0.38. Compressive strength (CS), tensile strength (TS), and flexural strength (FS) were evaluated at 7 and 28 days following IS standards. An optimal combination of 10–15% SF and 20–40% WGA achieved peak 28-day values of 72.8 MPa (CS), 6.02 MPa (TS), and 9.35 MPa (FS), exceeding M60-grade benchmarks by 10–15%. To predict mechanical properties, ensemble machine learning models including Random Forest (RF) and Gradient Boosting (GB) were developed. Among these, the GB model demonstrated superior predictive performance across all output variables, achieving higher coefficients of determination (R² = 0.9813, 0.9803, and 0.9791 for CS, TS, and FS, respectively) along with lower prediction errors (MAE and RMSE) compared to RF. Feature importance analysis revealed that cement content and SF were the most influential parameters governing strength development. The results indicate that optimized SF–WGA concrete can provide a sustainable alternative to conventional HSC, while GB-based models offer a reliable approach for strength prediction.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3947 - 3959"},"PeriodicalIF":0.0,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458705","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 seismic damage limit state models for corroded low-ductility RC buildings","authors":"M. K. Athul Nath,&nbsp;N. R. Chirdeep,&nbsp;Needhi Kotoky","doi":"10.1007/s42107-026-01698-4","DOIUrl":"10.1007/s42107-026-01698-4","url":null,"abstract":"<div><p>Corrosion-induced deterioration significantly compromises the seismic performance of reinforced concrete (RC) frame buildings, and the risk is compounded for low-ductility buildings constructed prior to the introduction of seismic design codes, yet robust tools for predicting their performance levels remain limited. This study proposes a five-step machine learning (ML)-based framework to predict damage state thresholds for corroded low-ductility RC frames. A finite element (FE) model capable of considering variability in material, geometric, and non-uniform corrosion deterioration is developed. An extensive dataset is generated using Quasi-random sequence, covering a broad range of structural, material, and deterioration parameters. Nonlinear static pushover analyses are conducted to monitor component-level damages and map into global-level responses in terms of maximum inter-story drift (<i>MIDR</i>) limits corresponding to five damage states. The damage states correspond to yielding of reinforcement, spalling of cover concrete, crack formation in joints, and shear failure of RC members. An Artificial Neural Network (ANN) regression model, optimized using Bayesian hyperparameter tuning, is trained and evaluated using five-fold cross-validation. The model achieved <i>R</i>² values ranging from 0.81 to 0.98, demonstrating strong and consistent predictive accuracy across all damage states. To facilitate practical adoption, a Python-based graphical user interface (GUI) is developed, enabling engineers to obtain real-time <i>DS</i> threshold predictions without requiring expertise in ML or FE modeling. The proposed framework offers a computationally efficient alternative to conventional methods for seismic performance assessment of aging RC structures.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3911 - 3926"},"PeriodicalIF":0.0,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148459162","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":"Nonlinear seismic performance of LRB-isolated buildings under near-field ground motions: influence and limits of maximum isolator displacement","authors":"Yao Domadzra,&nbsp;Mohit Bhandari,&nbsp;Vijay Sharma","doi":"10.1007/s42107-026-01709-4","DOIUrl":"10.1007/s42107-026-01709-4","url":null,"abstract":"<div><p>This study introduces a methodology for monitoring the seismic performance of base-isolated buildings by investigating the influence of varying maximum isolator displacement from 100 mm to 600 mm on the nonlinear response of the base-isolated building under near-field earthquake ground motions. This research study explores the dynamic variation of maximum isolator displacement (D_max) and its effect on key seismic performance indicators. For this purpose, a 5-storey reinforced concrete building frame is selected with lead rubber bearing as an isolation system. The non-linear time history analysis is carried out to evaluate its impact on the overall response of the base isolator. The results revealed a decreasing energy dissipation as the isolator displacement increased. The base shear is decreased by 51.3%, storey displacement reduced by 68.7%, interstorey drift ratio is reduced by 55%, storey acceleration reduced by 68%, and plastic hinge rotation reduced by 77.71% in a base-isolated (BI) building as compared to a fixed base. Moreover, the results highlight the existence of isolator displacement beyond which the response becomes saturated and adds less benefit. The insights gained provide valuable guidance for performance-based design of lead rubber bearing systems, offering a critical step toward advancing the safety, cost-effectiveness, and seismic resilience of base-isolated buildings globally.</p></div>","PeriodicalId":8513,"journal":{"name":"Asian Journal of Civil Engineering","volume":"27 8","pages":"3927 - 3946"},"PeriodicalIF":0.0,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148459166","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}