This is Urban Machine. We Use Robotics and AI to Reclaim Lumber for Reuse. Urban Machine pioneers sustainable wood reclamation. Our robotic solution removes metal fasteners from C&D lumber waste, reclaiming it for premium wood products.We're in business to transform the construction industry by reducing wood waste and promoting …

Description. Application. Emerging Issues. Relevant Codes and Standards. Additional Resources. Responsible management of waste is an essential aspect of sustainable building. In this context, managing …

Machine-learning classification was also employed by Lin et al. (2022), who recognized visually different CDW fragments and achieved an accuracy ranging between 75 and 80%. The closest to our goal is the study by Hoong et al. (2020), who employed neural networks for the classification of recycled aggregates. They constructed a library of …

The application of machine learning contributes to intelligent and efficient management of construction and demolition waste, leading to a reduction in waste generation and an increased emphasis on recycling. This research conducts a comprehensive analysis of 98 journals related to the application of machine learning in …

The results reveal that machine learning is applied in four main areas and 15 subfields, specifically focusing on construction and demolition waste generation, on-site handling, transportation, and disposal. Various models, such as artificial neural networks, deep learning, convolutional neural networks, and support vector machines, demonstrate ...

Digital construction management tools integrate AI to analyze vast amounts of data, streamline processes, and provide actionable insights. These tools enable construction projects to be more agile, responsive, and efficient in their use of materials, leading to significant reductions in waste and enhanced recycling efforts.

Construction equipment operators keep construction sites humming. They go by many names, including heavy equipment operator, construction machine operator, construction operator or operating engineer. They …

As can be seen from Fig. 9, the inherent relation between multiple project characteristics and waste generation should be established to develop a custom machine learning algorithm for predicting construction waste for new projects. The inputs from the previous layer and the selected AI algorithm will enable the development of analytical …

Responsible management of waste is an essential aspect of sustainable building. In this context, managing waste means eliminating waste where possible; minimizing waste where feasible; and reusing …

Construction waste recycling (CWR) is an effective strategy to avoid CW disposal in landfills ( Ulubeyli et al., 2017 ). Also, CWR is a waste management strategy that is preferable and environmentally more advisable than disposing at landfills ( Brum et al., 2021 ). Furthermore, CWR converts CW into new materials for use ( Bao and Lu, 2021 ).

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Huge quantities of construction and demolition waste ( C & D) is produced all over the world. Ecohog's recycling systems are designed to process and proficiently separate …

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Machine learning is the science (and art) of programming computers so they can learn from data [30].Fig. 1 shows the workflow of traditional machine learning implementation, starting with raw data and proceeding to data preparation, which involves data processing and feature extraction. Subsequently, the data is partitioned into training and test datasets.

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Building a pattern-recognition or machine learning framework includes cautious designing and domain expertise to plan an element extractor that can change raw images data into an element vector that can be utilised by a classifier (e.g., Support Vector Machine (SVM) and k-Nearest Neighbors (k−NN)) to detect or determine patterns in the …

Construction and demolition waste contains in large quantities asphalt, bricks and concrete (known as ABC). Asphalt paving materials can be crushed and reassembled with aggregates and emulsifiers to make new …

The mobile C&D waste recycling system is suitable for crushing, sorting and recycling construction waste at the demolition site or landfill. It can be moved at any time when needed, which helps reduce transportation costs. The mobile system can be composed of mobile crushers, mobile shredders, and mobile screening equipment.

Construction waste sorting machine. Construction waste refers to the general term of sludge, waste concrete, waste brick and other wastes produced by people in the production activities of construction industry such as demolition, construction, decoration and maintenance. According to the classification of production sources, …

Few studies reported using the convolutional neural network with transfer learning to finely classify the construction and demolition waste. This study aims to develop a highly efficient method to realize the finely sorting the construction and demolition waste, which is a key step for promoting the recycling system to realize carbon neutrality in the …

The review discovers a trend of shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were unevenly distributed in different sectors such as , commerce and institution, …

It can be seen that the machine learning (ML) models, such as multiple linear regression (MLR), grey model (GM), artificial neural network (ANN), and decision tree (DT), are among the most frequently adopted FM methods. ML is a computer program that "optimize a performance criterion using example data or past experience" (Alpaydin, …

On the other hand, these studies examined machine learning methods for applications in only one subtopic of extensive researches in IWIR such as plastic or dry-mechanical recycling of non-hazardous waste. Therefore, a comprehensive and systematic review focusing on the broad applications of CNNs in IWIR is of important significance.

The process of developing a machine learning model is an iterative one that involves evaluating the model's performance on a test set in real-world scenarios (Azadi & Karimi-Jashni, 2015).It is important to note that adjustments may need to be made to the model's accuracy and reliability to achieve the desired level of performance.

At a construction site, a Maçonnerie Gratton employee loads a brick onto a machine's slide. With the push of a button, the brick vanishes into the metallic box and, within seconds, comes out ...

The estimation of construction and demolition waste is a difficult task, because it depends on technical, cultural and geometric variables of the buildings. Machine learning has been increasingly used in the construction industry, because it combines the calculation of large amounts of data with the difficulty of describing or understanding …

Xiao et al. developed a machine to automatically sort C&D waste on a conveyer belt [15]. Kujala et al. designed a truss-type robot for heavy objects grasping on a conveyer belt [16]. In comparison, mature technology for on-site C&D waste recycling has long been absent. Highly autonomous C&D waste recycling on construction sites …

Urban Machine is working to recover waste wood from construction and demolition sites. Image courtesy Urban Machine. "If we can capture 50% of the waste, that's an $18 billion opportunity each ...

What you need is dependable, durable, high-performance equipment tailored to your specific needs. Machinex Construction & Demolition Recycling Systems maximize recovery rates and material quality, as well …