AIMC Topic: Construction Materials

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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
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 ...

Leveraging Building Material as Part of the In-Plane Robotic Kinematic System for Collective Construction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
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...

Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS.

PloS one
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 ...

Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment.

Environmental science and pollution research international
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...

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy.

Molecules (Basel, Switzerland)
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...

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)
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...

Multi-Level-Phase Deep Learning Using Divide-and-Conquer for Scaffolding Safety.

International journal of environmental research and public health
A traditional structural analysis of scaffolding structures requires loading conditions that are only possible during design, but not in operation. Thus, this study proposes a method that can be used during operation to make an automated safety predi...

Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics.

Journal of the Royal Society, Interface
Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application ...

A hybrid hierarchical soft computing approach for the technology selection problem in brick industry considering environmental competencies: A case study.

Journal of environmental management
Brick manufacturing is an important industry which produces some fundamental building materials. Since brick production industries have environmental adverse effects such as air pollution, excessive energy consumption as well as waste production, the...

Network stiffness: A new topological property in complex networks.

PloS one
Aiming at serving the interdisciplinary demand in network science, this paper introduces a new concept for complex networks, named network stiffness, which is extracted from structural engineering by assuming that a complex network behaves similarly ...