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Neural-based prediction of structural failure of multistoried RC buildings
Sirshendu Hore,Sankhadeep Chatterjee,Sarbartha Sarkar,Nilanjan Dey,Amira S. Ashour,Dana Bălas-Timar,Valentina E. Balas 국제구조공학회 2016 Structural Engineering and Mechanics, An Int'l Jou Vol.58 No.3
Various vague and unstructured problems encountered the civil engineering/ designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.
Sirshendu Majumdar 한국예이츠학회 2023 한국예이츠 저널 Vol.70 No.-
This paper examines in some detail the relationship between Elizabeth Corbet Yeats and her brother, William Butler Yeats, pertaining to the activities of the Cuala Press which Elizabeth ran as the printer from 1902 (as the Dun Emer Press and then, the Cuala Press from 1908) while William was the editor, selecting and soliciting books. Their relationship, while stormy all-along, and determined by William’s authoritative editorial choices undermining his sister’s literary role, was, nonetheless, mitigated by William’s attribution of the key role as the printer and publisher to Elizabeth. The relationship was thus often ambivalent and elusive, like many such family ties, than has generally been acknowledged by scholars.
Sankhadeep Chatterjee,Sarbartha Sarkar,Sirshendu Hore,Nilanjan Dey,Amira S. Ashour,Fuqian Shi,Dac-Nhuong Le 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.63 No.4
Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multiobjective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLPFFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.
Chatterjee, Sankhadeep,Sarkar, Sarbartha,Hore, Sirshendu,Dey, Nilanjan,Ashour, Amira S.,Shi, Fuqian,Le, Dac-Nhuong Techno-Press 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.63 No.4
Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.
Erucic Acid Production Using Porcine Pancreas Lipase: Enhancement by Mixed Surfactants
Debajyoti Goswami,Jayanta Kumar Basu,Sirshendu De 한국생물공학회 2011 Biotechnology and Bioprocess Engineering Vol.16 No.2
Application of mixed surfactants coupled with statistical optimization in lipase catalyzed oil hydrolysis is presented for the first time in this study. Selective hydrolysis of brown mustard oil to erucic acid by porcine pancreas lipase was enhanced by mixed surfactants comprising of an oil-soluble nonionic surfactant (Span 80) and a watersoluble nonionic surfactant (Tween 80). The production of erucic acid was maximized using statistically designed experiments and subsequent analysis of their result by response surface methodology. The most significant variables were enzyme concentration and concentration of Tween 80. Small changes in pH and concentration of Span 80 also produced a significant change in the production of erucic acid. Temperature and speed of agitation were insignificant variables and were fixed at 35℃ and 900 rpm, respectively. Under these conditions, the optimal combination of other variables were pH 9.65, 2.13 mg/g enzyme in oil, 9.8× 10^(−3) M Span 80 (in oil), and 4 × 10^(−3) M Tween 80 (in buffer). These conditions led to formation of 99.69% of the total erucic acid in 1.25 h. Interaction of enzyme concentration with pH significantly affected erucic acid production.
Chaudhuri, Suhnrita,Acharya, Sagar,Chatterjee, Sirshendu,Kumar, Pankaj,Singh, Manoj Kumar,Bhattacharya, Debanjan,Basu, Anjan Kumar,Dasgupta, Shyamal,Flora, S.J.S.,Chaudhuri, Swapna Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.6
Arsenic exposure is a serious health hazard worldwide. We have previously established that it may result in immune suppression by upregulating Th2 cytokines while downregulating Th1 cytokines and causing lymphocytic death. Treatment modalities for arsenic poisoning have mainly been restricted to the use of chelating agents in the past. Only recently have combination therapies using a chelating agent in conjunction with other compounds such as anti-oxidants, micronutrients and various plant products, been introduced. In the present study, we used T11TS, a novel immune potentiating glycopeptide alone and in combination with the sulfhydryl-containing chelator, mono-iso-amyl-dimarcaptosuccinic acid (MiADMSA) as a therapeutic regimen to combat arsenic toxicity in a mouse model. Results indicated that Th1 cytokines such as TNF-${\alpha}$, $IFN{\gamma}$, IL12 and the Th2 cytokines such as IL4, IL6, IL10 which were respectively downregulated and upregulated following arsenic induction were more efficiently restored to their near normal levels by T11TS alone in comparison with the combined regimen. Similar results were obtained with the apoptotic proteins studied, FasL, BAX, BCL2 and the caspases 3, 8 and 9, where again T11TS proved more potent than in combination with MiADMSA in preventing lymphocyte death. The results thus indicate that T11TS alone is more efficient in immune re-establishment after arsenic exposureas compared to combination therapy with T11TS+MiADMSA.
Debajyoti Goswami,Jayanta Kumar Basu,Sirshendu De 한국생물공학회 2009 Biotechnology and Bioprocess Engineering Vol.14 No.2
In this study, castor oil is hydrolyzed in presence of Candida rugosa lipase, while in the buffer (aqueous) phase as a dispersion medium. The following conditions were used to optimize the process: speed of agitation, initial pH of buffer phase, temperature, and ratio of buffer phase volume to oil weight. The optimal conditions are 1,100 rpm, pH 6.5, temperature 35°C, and 3:1 buffer phase volume to oil weight ratio. Under these described conditions, the reusability of lipase was tested and it was found that nearly 80% of original hydrolysis percentage was achieved after the first recycle In this study, castor oil is hydrolyzed in presence of Candida rugosa lipase, while in the buffer (aqueous) phase as a dispersion medium. The following conditions were used to optimize the process: speed of agitation, initial pH of buffer phase, temperature, and ratio of buffer phase volume to oil weight. The optimal conditions are 1,100 rpm, pH 6.5, temperature 35°C, and 3:1 buffer phase volume to oil weight ratio. Under these described conditions, the reusability of lipase was tested and it was found that nearly 80% of original hydrolysis percentage was achieved after the first recycle