Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm

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.

Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm

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.

Neuropharmacological assessment of $Curcuma$ $caesia$ rhizome in experimental animal models

Karmakar, Indrajit,Saha, Pathik,Sarkar, Nilanjan,Bhattacharya, Sanjib,Haldar, Pallab K. 경희한의학연구센터 2011 Oriental Pharmacy and Experimental Medicine Vol.11 No.4

$Curcuma$ $caesia$ Roxb. (Zingiberaceae), called black turmeric in English, is a perennial herb found throughout the Himalayan region, North-East and Central India. The plant has been traditionally used in India for several medicinal purposes. The present study was carried out to evaluate the methanol extract of $C.$ $caesia$ rhizome (MECC) for some neuropharmacological activities in experimental animal models. MECC (at 50 and 100 mg/kg body weight) was evaluated for analgesic activity by acetic acid-induced writhing and tail flick tests. Locomotor activity was measured by means of an actophotometer. Anticonvulsant property was assessed against pentylenetetrazol-induced convulsion in mice and muscle relaxant effect was evaluated by using rota-rod apparatus. The results of the present study revealed remarkable analgesic, locomotor depressant, anticonvulsant and muscle relaxant effects of $C.$ $caesia$ rhizome, demonstrating depressant action on the central nervous system. The outcome of present study can validate certain traditional uses of $C.$ $caesia$ rhizome in India.

Neuropharmacological assessment of Curcuma caesia rhizome in experimental animal models

Indrajit Karmakar,Pathik Saha,Nilanjan Sarkar,Sanjib Bhattacharya,Pallab K. Haldar 경희대학교 융합한의과학연구소 2011 Oriental Pharmacy and Experimental Medicine Vol.11 No.4

Curcuma caesia Roxb. (Zingiberaceae), called black turmeric in English, is a perennial herb found throughout the Himalayan region, North-East and Central India. The plant has been traditionally used in India for several medicinal purposes. The present study was carried out to evaluate the methanol extract of C. caesia rhizome (MECC)for some neuropharmacological activities in experimental animal models. MECC (at 50 and 100 mg/kg body weight)was evaluated for analgesic activity by acetic acid-induced writhing and tail flick tests. Locomotor activity was measured by means of an actophotometer. Anticonvulsant property was assessed against pentylenetetrazol-induced convulsion in mice and muscle relaxant effect was evaluated by using rota-rod apparatus. The results of the present study revealed remarkable analgesic, locomotor depressant, anticonvulsant and muscle relaxant effects of C. caesia rhizome,demonstrating depressant action on the central nervous system. The outcome of present study can validate certain traditional uses of C. caesia rhizome in India.

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.

Scavenging activity of $Curcuma$ $caesia$ rhizome against reactive oxygen and nitrogen species

Karmakar, Indrajit,Dolai, Narayan,Saha, Pathik,Sarkar, Nilanjan,Bala, Asis,Haldar, Pallab Kanti 경희한의학연구센터 2011 Oriental Pharmacy and Experimental Medicine Vol.11 No.4

$Curcuma$ $caesia$ Roxb. (Zingiberaceae), known as black turmeric in English, is a perennial herb found throughout the Himalayan region, North-East and Central India. The plant has been traditionally used in India for several medicinal purposes. Present study was carried out to evaluate the methanol extract of $C.$ $caesia$ (MECC) rhizome for some $in$ $vitro$ antioxidant studies as because we know that many diseases are associated with reactive oxygen species (ROS) and reactive nitrogen species (RNS). Effect of MECC on ROS and RNS were evaluated in different $in$ $vitro$ methods like 1, 1-diphenyl-2-picrylhydrazil radical, hydroxyl radicals, superoxide anions, nitric oxide, hydrogen peroxide, peroxynitrite and hypochlorous acid. Lipid peroxidation, total phenolic content was also measured by standard assay method. The extract showed significant antioxidant activities in a dose dependent manner. The $IC_{50}$ values for scavenging of free radicals were $94.03{\pm}0.67{\mu}g/ml$, $155.59{\pm}3.03{\mu}g/ml$, $68.10{\pm}1.24{\mu}g/ml$, $21.07{\pm}1.78{\mu}g/ml$, $260.56{\pm}12.65{\mu}g/ml$ and $33.33{\pm}0.52{\mu}g/ml$ for DPPH, nitric oxide, superoxide, hydroxyl, peroxynitrite and hypochlorous acid respectively. Reductive ability of the extract was also tested where dose dependent reducing capability was observed. The rhizome extract contains 677.7 ${\mu}g$ of phenolic compound in 10 mg of the extract which is accounted for its free radical as well as antioxidant activity. From the above study it is concluded that the methanol extract of $C.$ $caesia$ rhizome is a potential source of natural antioxidant.

Scavenging activity of Curcuma caesia rhizome against reactive oxygen and nitrogen species

Indrajit Karmakar,Narayan Dolai,Pathik Saha,Nilanjan Sarkar,Asis Bala,Pallab Kanti Haldar 경희대학교 융합한의과학연구소 2011 Oriental Pharmacy and Experimental Medicine Vol.11 No.4

Curcuma caesia Roxb. (Zingiberaceae), known as black turmeric in English, is a perennial herb found throughout the Himalayan region, North-East and Central India. The plant has been traditionally used in India for several medicinal purposes. Present study was carried out to evaluate the methanol extract of C. caesia (MECC) rhizome for some in vitro antioxidant studies as because we know that many diseases are associated with reactive oxygen species (ROS) and reactive nitrogen species (RNS). Effect of MECC on ROS and RNS were evaluated in different in vitro methods like 1, 1-diphenyl-2-picrylhydrazil radical, hydroxyl radicals, superoxide anions, nitric oxide, hydrogen peroxide,peroxynitrite and hypochlorous acid. Lipid peroxidation,total phenolic content was also measured by standard assay method. The extract showed significant antioxidant activities in a dose dependent manner. The IC_(50) values for scavenging of free radicals were 94.03±0.67 μg/ml, 155.59±3.03 μg/ml,68.10±1.24 μg/ml, 21.07±1.78 μg/ml, 260.56±12.65 μg/ml and 33.33±0.52 μg/ml for DPPH, nitric oxide, superoxide,hydroxyl, peroxynitrite and hypochlorous acid respectively. Reductive ability of the extract was also tested where dose dependent reducing capability was observed. The rhizome extract contains 677.7 μg of phenolic compound in 10 mg of the extract which is accounted for its free radical as well as antioxidant activity. From the above study it is concluded that the methanol extract of C. caesia rhizome is a potential source of natural antioxidant.