Increasing the Safety in Recycling of Construction and Demolition Waste by Using Supervised Machine Learning

Kuritcyn, Petr; Anding, Katharina; Linß, Elske Ing.; Latyev, Svyatoslav

doi:10.1088/1742-6596/588/1/012035

Cited by 22 publications

(8 citation statements)

References 1 publication

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“…It consists of a large number of decision trees, a so-called decision forest, and thus belongs to the ensemble methods 11 . This classifier achieves very good results in a short time, even on complex, highly nonlinear recognition tasks 12,13 . Furthermore, it has only a few parameters to be set.…”

Section: Classical Methods Of Machine Learningmentioning

confidence: 97%

Comparison of the performance of innovative deep learning and classical methods of machine learning to solve industrial recognition tasks

Anding

Haar

Polte

et al. 2019

Photonics and Education in Measurement Science 2019

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Artificial intelligence and machine learning are becoming increasingly important in science and society. In image processing, they are mainly used for object classification. The aim of this paper is the comparison of classical supervised machine learning methods with innovative deep learning (DL) approaches in terms of performance, which is described by the calculated accuracy. Classifiers of different characteristics are used. These are the Support Vector Machines, Random Forest, k-Nearest-Neighbor, and Naive Bayes. They are compared to two not pre-trained and four pre-trained neural networks. The former neural network are based on LeNet, the second ones include AlexNet, GoogleNet and ResNet provided by Matlab as well as a pre-trained neural network provided by MVTec HALCON. Comparisons were made using the recognition rates achieved with five real data sets from industrial applications. The results showed that not pre-trained neural networks produce worse results than classical classifiers with the given small amounts of data for training. On the other hand, the pre-trained networks achieved surpassing recognition rates. However, if there are features that describe the classes very well, the recognition performance of classical machine learning methods little differs from that of deep learning algorithms.

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Section: Classical Methods Of Machine Learningmentioning

confidence: 97%

Comparison of the performance of innovative deep learning and classical methods of machine learning to solve industrial recognition tasks

Anding

Haar

Polte

et al. 2019

Photonics and Education in Measurement Science 2019

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show abstract

“…Water efficiency in the classification of waste was also tested by few studies 27 , 28 , one of them demonstrated the excellent quality of RF, Nu‐ and C‐LibSVM, with accuracy above 90 % 27 .…”

Section: Applications Of Artificial Intelligence: Waste Managementmentioning

confidence: 99%

A Study on AI‐based Waste Management Strategies for the COVID‐19 Pandemic

et al. 2022

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COVID-19 has swept across the globe and disrupted all vectors of social life. Every informed measure must be taken to stop its spread, bring down number of new infections and move to normalization of daily life. Contemporary research has not identified waste management as one of the critical transmission vectors for COVID-19 virus. However, most underdeveloped countries are facing problems in waste management processes due to the general inadequacy and inability of waste management. In that context, smart intervention will be needed to contain possibility of the COVID-19 spread due to inadequate waste management. This paper presents a comparative study of the artificial intelligence/ machine learning based techniques, and potential applications in the COVID-19 waste management cycle (WMC). A general integrated solid waste management (ISWM) strategy is mapped for both short-term and long-term goals of COVID-19 WMC, making use of the techniques investigated. By aligning current health/waste-related guidelines from health organizations and governments worldwide and contemporary, relevant research in area, the challenge of COVID-19 waste management and, subsequently, slowing the pandemic down may be assisted.

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“…When the C&DW are at a recycling site, a significant challenge pertains to the classification of different categories of waste. The researchers have addressed this problem through image processing (Anding et al, 2011) and infrared spectrum (Kuritcyn et al, 2015) methods in a supervised learning setup. In smart city designs, the framework for identifying and processing potential C&DW is given by (Sartipi, 2020).…”

Section: Demolition and Recyclingmentioning

confidence: 99%

Construction 4.0: what we know and where we are headed?

Karmakar¹,

Delhi²

2021

ITcon

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The last decade has witnessed unprecedented changes in the technologies and processes involved in the construction industry. The philosophies associated with Industry 4.0 now reverberate in construction 4.0. Digitalization and interconnectivity in the cyber-physical systems of the sector are at the heart of such transformation. Construction 4.0 brings to the table a plethora of technologies and associated processes over the construction project lifecycle. The current study performs a state-of-art literature review to summarize the knowledge advancement in construction 4.0. A layered conceptualization spanning across project lifecycle utilizing the people-process-technology dimensions is presented to summarize the current understanding of Construction 4.0. The cyber-physical space is classified into the physical, digital tool, data, and core data security and interoperability layers. The inter-layer and intra-layer interactions and information flows are then conceptualized based on the extant literature, including the human interaction and interventions. The people-process-technology dimensions were discussed across the project lifecycle through interactions in these layers. It is observed that Construction 4.0 is set to be driven by data creation, data flow, data transformation, and data storage across the project lifecycle to ensure a collaborative environment across the stakeholders who interact and associate with different layers of Construction 4.0. The article finally presents challenges with the current formulations and explores ways to further our knowledge in the area.

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Increasing the Safety in Recycling of Construction and Demolition Waste by Using Supervised Machine Learning

Cited by 22 publications

References 1 publication

Comparison of the performance of innovative deep learning and classical methods of machine learning to solve industrial recognition tasks

Comparison of the performance of innovative deep learning and classical methods of machine learning to solve industrial recognition tasks

A Study on AI‐based Waste Management Strategies for the COVID‐19 Pandemic

Construction 4.0: what we know and where we are headed?

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