Asian Journal of Civil Engineering -

Sujet:	Early View

Articles dans ce numéro

Auteur(s)	Titre	Page(s)
Hayatdavoodi, Aliakbar / Li, Yancheng	An analytical method to determine the ultimate shear strength of continuous steel plate girders
Nimer, Hamsa / Ismail, Rabah / Al-Mattarneh, Hashem / Khodier, Mohanad / Jaradat, Yaser / Rawashdeh, Adnan / Rawashdeh, Mohammad	Artificial neural networks and noncontact microwave NDT for evaluation of polypropylene fiber concrete
Habal, Adel Hassan Yahya / Benbouras, Mohammed Amin	California bearing ratio and compaction parameters prediction using advanced hybrid machine learning methods
George, Christo / Kumar, Rakesh / Ramaraju, H. K.	Comparison of experimental and analytical studies in light gauge steel sections on CFST using SFRC in beams subjected to high temperatures
Joshi, Bhavesh / Sudhakaran, Pratheek / Varma, Manish	Comprehensive study of sequester-based carbon concrete in an acidic environment using artificial neural networks
Chakali, Dathathreya / Pollayi, Hemaraju / Rao, Praveena	Correction: IoT based structural health monitoring of bridges using wireless sensor networks
Satarkar, Priti R. / Dixit, Pradnya R. / Londhe, Shreenivas N.	Damage classification and segmentation in extended shear tab connection using convolutional neural networks and transfer learning
Tariq, Jawad / Hu, Kui / Gillani, Syed Tafheem Abbas / Chang, Hengyu / Ashraf, Muhammad Waqas / Khan, Adnan	Enhancing the predictive accuracy of recycled aggregate concrete’s strength using machine learning and statistical approaches: a review
salama, Ahmed	Evaluating the impact of construction delays on project duration using machine learning and multi-criteria decision analysis
Ahmed, Ahmed Ramadan / Temimi, Feras A. R. / Yermoshin, N. A.	Experimental verification of the compressive stiffness of high-damping rubber bearings (HDRB) according to STN EN 1337-3 and their effectiveness in seismic isolation for cable-stayed bridges
Kumar, Prasenjit / Yadav, Prince / Singh, Vikash	Exploring steel fiber integration in dry lean concrete: predictive analysis of compressive strength and performance via machine learning
Subedi, Ajaya / Thapa, Bhum Bahadur / Poudel, Ashish / Adhikari, Binaya / Khadka, Binod / Poudel, Samrat / Sapkota, Sanjog Chhetri	Exploring the potential of Himalayan Giant Nettle fiber and supplementary cementitious materials for sustainable concrete development
Ras, Abdelouahab	Far-field earthquake response examination of RC buildings equipped with fluid viscous dampers
Hemalatha, C. / Mohammed, M. S. Haji Sheik / Vijayaraghavan, Roopa / Navabshan, Irfan / Abdul Kudus, Sakhiah	Influence of chloride acceleration methods on the electrochemical behaviour of superplasticiser admixed reinforced cementitious systems
Meena, Rahul Kumar / Pratap, Ajay / Raj, Ritu / Anbukumar, S.	Influence of corner geometry on wind-induced forces in tall building models
Das, Soumyadip / Datta, Aloke Kumar / Topdar, Pijush / Pal, Apurba	Innovative approaches to concrete health monitoring: wavelet transform and artificial intelligence models
Al-Ghabawi, Humam Hussein Mohammed / Resheq, Ali Sadiq / Almuhsin, Bayrak S.	Machine learning based prediction for maximum base shear, top displacement, and vibration period for SCBF under nonlinear response history analysis
Polu, Sathish / Sivakumar, M. V. N. / Pancharathi, Rathish Kumar	Machine learning-driven advancements in structural health monitoring: comprehensive multi-state classification for three-dimensional structures
Mishra, Divyansh / Jha, Govinda / Pal, Priyaranjan	Mitigating seismic effects on frame structures with inerter system
Oyebisi, Solomon / Shammas, Mahaad Issa / Oladeji, Samuel Olawale / Sani, Reuben	Modelling mechanical strengths of blended cement concrete incorporating corncob ash and calcite powder: experimental and machine learning approaches
Walimbe, Sonali / Nandineni, Rama Devi / Rege, Sumita	Multi-objective optimization of school environments to foster nature connectedness using NSGA-III in school design
Ashraf, Jawad / Azam, Rafi / Rifa, Asfia Akter / Rana, Md Jewel	Multiple machine learning models for predicting annual energy consumption and demand of office buildings in subtropical monsoon climate
Singh, Sabhilesh / Anand, Vivek	Optimizing self-compacting concrete with steel slag and fiber additions: enhancing fresh, mechanical, durability, and microstructural properties
Pande, Prashant B. / Dhengare, Sagar W. / Raut, Jayant M. / Bhagat, Rajesh M. / Bahoria, Boskey V. / Shelke, Nilesh / Upadhye, Sachin D. / Vairagade, Vikrant S.	Optimizing sustainability and resilience of composite construction materials using life cycle assessment and advanced artificial intelligence techniques
Behera, Amir Prasad / Chauhan, Mayank / Shrivastava, Gaurav / Singh, Prachi / Shukla, Jyoti / Sethi, Krushna Chandra	Optimizing trade-off between time, cost, and carbon emissions in construction using NSGA-III: an integrated approach for sustainable development
Dejin, Chen / S. Zainal, S.M. Iqbal / Dullah, Salinah / Rizalman, Ahmad Nurfaidhi / Harith, Noor Sheena Herayani	Performance of perforated double steel plate reinforcements in composite shear walls under axial compression
Alsharari, Fahad	Predicting the compressive strength of fiber-reinforced recycled aggregate concrete: A machine-learning modeling with SHAP analysis
Hoque, Md Ahatasamul / Shrestha, Ajad / Sapkota, Sanjog Chhetri / Ahmed, Asif / Paudel, Satish	Prediction of autogenous shrinkage in ultra-high-performance concrete (UHPC) using hybridized machine learning
Pathak, Sudhanshu S. / Mane, Sachin J. / Vesmawala, Gaurang R. / Sarnobat, Sandeep S.	Prediction of compressive strength of concrete using multilayer perception network, generalized feedforward network, principal component analysis network, time lagged recurrent network, recurrent network
Sonker, Maheshwari / Shanker, Rama	Prediction of durability of the concrete using electro-mechanical impendence technique: an experimental and machine learning approaches
Gahlot, Sumit / Mangal, Rajat / Arya, Abhishek / Sethy, Barada Prasad / Sethi, Krushna Chandra	Prediction of swelling pressure of expansive soil using machine learning methods
Ahiwale, Dhiraj D. / Patil, Ajit N. / Kontoni, Denise-Penelope N.	Quantification and parametric analysis of large overhang RC planar frames subjected to horizontal and combined horizontal–vertical earthquake loading
Govardhan Naik, B. / Nakkeeran, G. / Roy, Dipankar	Recycling glass waste into concrete aggregates: enhancing mechanical properties and sustainability
Bouzidi, M. A. / Bouziane, A. / Bouzidi, N.	RSM, SVM and ANN modeling of the properties of self-compacting concrete with natural mordenite-rich tuff and recycled glass
Yatnatti, Mohammed Hanif / Patil, M. B.	Seismic response control of RC buildings by using tuned liquid dampers
Patil, Ashwini R. / Daterao, Smita / Jayale, Vivek	Seismic response control of RC structure using U- shape damper as isolator
Raoufy, Abdul Ali / Kheyroddin, Ali / Naderpour, Hosein	Seismic vulnerability assessment of hospital buildings in Kabul city (Afghanistan) using applied technology council (ATC – 21)
Tank, Yati R. / Vesmawala, G. R.	Serviceability assessment of lively skywalk footbridge under dynamic loadings of human motions in the vadodara metropolitan area
Arabasy, Mazin / Hussein, Mayyadah F. / Abu Osba, Rana / Al Dweik, Samah	Smart housing: integrating machine learning in sustainable urban planning, interior design, and development
Abu-shaikha, Ma’in	Smart sustainable architecture: leveraging machine learning for adaptive digital design and resource optimization
Sasikumar, P. / Govindh, M. A. / Subitha, T. / Ananthakumar, A.	A study on the flexural behaviour and self-healing of fibre reinforced bacterial concrete beams

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sur cette fiche
IssueID
28487
Publié(e) le:
28.06.2019
Modifié(e) le:
01.07.2019

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International Association for Bridge and Structural Engineering (IABSE)

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Asian Journal of Civil Engineering -

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Asian Journal of Civil Engineering -

Articles dans ce numéro

Vous avez dépassé votre limite de téléchargement mensuelle ! Les abonné·es à Structurae Plus peuvent télécharger 30 images ou ensembles de données par mois. Les utilisateurs·rices de Structurae Pro sont limités à 50.

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