Urban Train Soil-Structure Interaction Modeling and Analysis

Mohammadzadeh, Saeed; Karballaeezadeh, Nader; Mohemmi, Morteza; Mosavi, Amir; Várkonyi-Kóczy, Annamária R.

doi:10.20944/preprints201908.0162.v1

Cited by 11 publications

(3 citation statements)

References 12 publications

(13 reference statements)

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“…This trend has been also reported in other research domains, e.g. biofuels, agriculture, hydrology, and production [56][57][58][59][60][61][62][63][64].…”

Section: Building Energy Consumption Predictionsupporting

confidence: 79%

Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

Ardabili¹,

Mosavi

Várkonyi-Kóczy

2019

Preprint

Self Cite

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Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

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“…This trend has been also reported in other research domains, e.g. biofuels, agriculture, hydrology, and production [56][57][58][59][60][61][62][63][64].…”

Section: Building Energy Consumption Predictionsupporting

confidence: 79%

Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

Ardabili¹,

Mosavi

Várkonyi-Kóczy

2019

Preprint

Self Cite

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“…This paper further identified future trends in the advancement of learning algorithms for smart cities. The trend in smart cities have shown to follow the trend in the overall trend which is a shift toward the advancement of the more sophisticated hybrid, ensemble and deep learning models, as also shown in [80][81][82][83][84][85][86][87][88].…”

Section: Discussionmentioning

confidence: 99%

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Nosratabadi

Mosavi

Keivani

et al. 2020

Lecture Notes in Networks and Systems

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Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neurofuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

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“…Peran energi energi alternatif semakin berkembang seiring bertambahnya jumlah populasi manusia, yang menyebabkan peningkatan kebutuhan sektor energi [1]. Beberapa review penelitian tentang energi alternatif, terutama energi terbarukan telah dilakukan, seperti energi geothermal [2], energi angin [3], energi dari laut [4], energi biogas [5], biomasa [6], dan energi air [7]. Energi air atau PLTA memproduksi energi berdasarkan beda tinggi air (head) dan debit air setiap detiknya [7].…”

Section: Pendahuluanunclassified

Optimalisasi Daya Output Generator Pada Pico-Hydropower Menggunakan Transmisi Roda Gigi

Nurdin

Hastuti

2020

j. of. mechanical. engineering

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Generator menghasilkan listrik dengan optimal ketika sistem penggerak dari pembangkit berputar dengan arah dan kecepatan yang sesuai. Kecepatan putar sebuah pembangkit pico-hydro saat bekerja umumnya tidak sesuai dengan spesifikasi kerja dari generator. Transmisi roda gigi dapat diaplikasikan untuk menyesuaikan arah dan kecepatan putar dari pembangkit pico-hydro sesuai dengan spesifikasi kerja dari generator. Tujuan penelitian ini membandingkan kinerja pembangkit pico-hyro secara eksperimental dengan dan tanpa sebuah transmisi roda gigi. Perbandingan roda gigi input dan output 1 : 2,65, pengujian kinerja generator dengan mengukur kecepatan putar dengan tachometer, serta mengukur tegangan-arus yang mampu dihasilkan dengan multitester. Sedangkan pembebanan yang digunakan mengukur arus listrik menggunakan jumlah satu sampai tujuh buah lampu AC. Hasil penelitian ini menunjukkan kinerja generator dengan transmisi yang lebih tinggi dibandingkan tanpa transmisi roda gigi.

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Urban Train Soil-Structure Interaction Modeling and Analysis

Cited by 11 publications

References 12 publications

Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Optimalisasi Daya Output Generator Pada Pico-Hydropower Menggunakan Transmisi Roda Gigi

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