AIMC Topic: Construction Materials

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Sensor-based characterization of construction and demolition waste at high occupancy densities using synthetic training data and deep learning.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA Feb 22, 2024
Sensor-based monitoring of construction and demolition waste (CDW) streams plays an important role in recycling (RC). Extracted knowledge about the composition of a material stream helps identifying RC paths, optimizing processing plants and form the...
Neural Networks, Computer Algorithms Recycling Waste Management Deep Learning Construction Materials
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Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild.

Journal of environmental management Jan 1, 2024
The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting process...
Solid Waste Waste Management Construction Materials Construction Industry Industrial Waste Recycling Deep Learning
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The impact of thermal insulating materials in heat loss control in smart green buildings using experimental and swarm intelligent analysis.

Environmental science and pollution research international Oct 17, 2023
The efficacy of saving energy standards depends on the ability to anticipate the heat loss of buildings. Environmentally friendly materials, also known as eco-friendly or sustainable materials, have a minimal negative impact on the environment throug...
Neural Networks, Computer Algorithms Hot Temperature Construction Materials
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Real-time construction demolition waste detection using state-of-the-art deep learning methods; single-stage vs two-stage detectors.

Waste management (New York, N.Y.) Jun 1, 2023
Central to the development of a successful waste sorting robot lies an accurate and fast object detection system. This study assesses the performance of the most representative deep-learning models for the real-time localisation and classification of...
Deep Learning Construction Materials Construction Industry
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Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar.

PloS one Jan 23, 2023
Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious ...
Flexural Strength Glass Ionomer Cements Powders Artificial Intelligence Machine Learning Construction Materials
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Leveraging Building Material as Part of the In-Plane Robotic Kinematic System for Collective Construction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Jun 24, 2022
Although collective robotic construction systems are beginning to showcase how multi-robot systems can contribute to building construction by efficiently building low-cost, sustainable structures, the majority of research utilizes non-structural or h...
Robotic Surgical Procedures Construction Materials Biomechanical Phenomena Robotics Software
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Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS.

PloS one Dec 3, 2021
An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete containing Ground Granulated Blast Furnace Slag (GGBFS). To accomplish this purpose, an experimental database of ...
Monte Carlo Method Industrial Waste Humans Construction Materials Compressive Strength Neural Networks, Computer Materials Testing Reproducibility of Results
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Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment.

Environmental science and pollution research international Feb 15, 2021
One of the most critical parameters in concrete design is compressive strength. As the compressive strength of concrete is correctly measured, time and cost can be decreased. Concrete strength is relatively resilient to impacts on the environment. Th...
Deep Learning Construction Materials Compressive Strength Neural Networks, Computer Algorithms
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Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy.

Molecules (Basel, Switzerland) Feb 15, 2021
The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This r...
Neural Networks, Computer Artificial Intelligence Machine Learning Construction Materials Fires Flame Retardants
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A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns.

Molecules (Basel, Switzerland) Jul 31, 2020
In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas th...
Steel Engineering Models, Theoretical Machine Learning Construction Industry Databases, Factual Construction Materials Neural Networks, Computer Weight-Bearing
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