scholarly journals Application of Artificial Intelligence to Automate Construction Materials Data Classification

2019 ◽  
Author(s):  
Weerayut Chunyaem ◽  
Chayakrit Charoensiriwath ◽  
Kriengsak Panuwatwanich ◽  
Shigeki Saito ◽  
Patai Padungtin
Keyword(s):  
Artificial Intelligence ◽  
Construction Materials ◽  
Data Classification ◽  
Automate Construction

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

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1022
Author(s):  
Hoang T. Nguyen ◽  
Kate T. Q. Nguyen ◽  
Tu C. Le ◽  
Guomin Zhang
Keyword(s):  
Artificial Intelligence ◽  
Flame Retardancy ◽  
Material Science ◽  
Construction Materials ◽  
Engineering Problem ◽  
Future Research ◽  
Fire Performance ◽  
Structural Components ◽  
Comprehensive Overview ◽  
Fire Engineering

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 review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

scholarly journals Multi-sensor multi-resolution image fusion for improved vegetation and urban area classification

2015 ◽  
Vol XL-7/W4 ◽  
pp. 51-58 ◽  
Author(s):  
U. Kumar ◽  
C. Milesi ◽  
R. R. Nemani ◽  
S. Basu
Keyword(s):  
Data Fusion ◽  
Urban Area ◽  
San Francisco ◽  
Spatial Resolution ◽  
World View ◽  
Construction Materials ◽  
Data Classification ◽  
Spectral Unmixing ◽  
Landsat Data ◽  
Error Matrix

In this paper, we perform multi-sensor multi-resolution data fusion of Landsat-5 TM bands (at 30 m spatial resolution) and multispectral bands of World View-2 (WV-2 at 2 m spatial resolution) through linear spectral unmixing model. The advantages of fusing Landsat and WV-2 data are two fold: first, spatial resolution of the Landsat bands increases to WV-2 resolution. Second, integration of data from two sensors allows two additional SWIR bands from Landsat data to the fused product which have advantages such as improved atmospheric transparency and material identification, for example, urban features, construction materials, moisture contents of soil and vegetation, etc. In 150 separate experiments, WV-2 data were clustered in to 5, 10, 15, 20 and 25 spectral classes and data fusion were performed with 3x3, 5x5, 7x7, 9x9 and 11x11 kernel sizes for each Landsat band. The optimal fused bands were selected based on Pearson product-moment correlation coefficient, RMSE (root mean square error) and ERGAS index and were subsequently used for vegetation, urban area and dark objects (deep water, shadows) classification using Random Forest classifier for a test site near Golden Gate Bridge, San Francisco, California, USA. Accuracy assessment of the classified images through error matrix before and after fusion showed that the overall accuracy and Kappa for fused data classification (93.74%, 0.91) was much higher than Landsat data classification (72.71%, 0.70) and WV-2 data classification (74.99%, 0.71). This approach increased the spatial resolution of Landsat data to WV-2 spatial resolution while retaining the original Landsat spectral bands with significant improvement in classification.

scholarly journals Artificial Intelligence Methods and Their Applications in Civil Engineering

2015 ◽  
pp. 166-189 ◽  
Author(s):  
Gonzalo Martínez-Barrera ◽  
Osman Gencel ◽  
Ahmet Beycioglu ◽  
Serkan Subaşı ◽  
Nelly González-Rivas
Keyword(s):  
Artificial Intelligence ◽  
Graphical User Interface ◽  
Building Materials ◽  
Engineering Students ◽  
Civil Engineering ◽  
Construction Materials ◽  
Transportation Engineering ◽  
Concrete Technology ◽  
Promising Alternative ◽  
Fuzzy Genetic

Simulation of material properties generally involves the development of a mathematical model derived from experimental data. In structural mechanics and construction materials contexts, recent experiments have reported that fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithm (GA), and fuzzy genetic (FG) may offer a promising alternative. They are known as artificial intelligence (AI). In civil engineering, AI methods have been extensively used in the fields of civil engineering applications such as construction management, building materials, hydraulic, optimization, geotechnical and transportation engineering. Many studies have examined the applicability of AI methods to estimate concrete properties. This chapter described the principles of FL methods that can be taught to engineering students through MATLAB graphical user interface carried out in a postgraduate course on Applications of Artificial Intelligence in Engineering, discussed the application of Mamdani type in concrete technology and highlighted key studies related to the usability of FL in concrete technology.

scholarly journals Developing a Model to Estimate the Productivity of Ready Mixed Concrete Batch Plant

2020 ◽  
Vol 26 (10) ◽  
pp. 80-93
Author(s):  
Hussein T. Almusawi ◽  
Abbas M. Burhan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Comprehensive Evaluation ◽  
Construction Materials ◽  
Plant Production ◽  
Training Dataset ◽  
Ann Model ◽  
Batch Plant ◽  
Ready Mixed Concrete ◽  
Mixed Concrete

Productivity estimating of ready mixed concrete batch plant is an essential tool for the successful completion of the construction process. It is defined as the output of the system per unit of time. Usually, the actual productivity values of construction equipment in the site are not consistent with the nominal ones. Therefore, it is necessary to make a comprehensive evaluation of the nominal productivity of equipment concerning the effected factors and then re-evaluate them according to the actual values. In this paper, the forecasting system was employed is an Artificial Intelligence technique (AI). It is represented by Artificial Neural Network (ANN) to establish the predicted model to estimate wet ready mixed concrete (WRMC) plant production and dry ready mixed concrete (DRMC) plant production, in addition to determining the factors affecting productivity. The results showed that the artificial intelligence neural network is an effective technique to estimate the productivity of the dry and wet ready mixed concrete batch plant. The ANN model showed satisfying results of validation for both training and external datasets with the range of training dataset and poor results with the data that exceeds the range of training. At the same time, the skills of the operators, frequent failure of concrete, and lack of construction materials were the most important factor that affected productivity.

scholarly journals Artificial Intelligence Methods and Their Applications in Civil Engineering

Fuzzy Systems ◽  
2017 ◽  
pp. 1453-1477
Author(s):  
Gonzalo Martínez-Barrera ◽  
Osman Gencel ◽  
Ahmet Beycioglu ◽  
Serkan Subaşı ◽  
Nelly González-Rivas
Keyword(s):  
Artificial Intelligence ◽  
Graphical User Interface ◽  
Building Materials ◽  
Engineering Students ◽  
Civil Engineering ◽  
Construction Materials ◽  
Transportation Engineering ◽  
Concrete Technology ◽  
Promising Alternative ◽  
Fuzzy Genetic

Simulation of material properties generally involves the development of a mathematical model derived from experimental data. In structural mechanics and construction materials contexts, recent experiments have reported that fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithm (GA), and fuzzy genetic (FG) may offer a promising alternative. They are known as artificial intelligence (AI). In civil engineering, AI methods have been extensively used in the fields of civil engineering applications such as construction management, building materials, hydraulic, optimization, geotechnical and transportation engineering. Many studies have examined the applicability of AI methods to estimate concrete properties. This chapter described the principles of FL methods that can be taught to engineering students through MATLAB graphical user interface carried out in a postgraduate course on Applications of Artificial Intelligence in Engineering, discussed the application of Mamdani type in concrete technology and highlighted key studies related to the usability of FL in concrete technology.

Systematic review of application of artificial intelligence tools in architectural, engineering and construction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Hamza Momade ◽  
Serdar Durdyev ◽  
Dave Estrella ◽  
Syuhaida Ismail
Keyword(s):  
Artificial Intelligence ◽  
Structural Engineering ◽  
Construction Materials ◽  
Geographical Location ◽  
Engineering Project ◽  
Major Purpose ◽  
Content Type ◽  
Architectural Engineering ◽  
Engineering Project Management ◽  
Practical Implications

PurposeThis study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.Design/methodology/approachA thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.FindingsThe following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.Practical implicationsThe findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.Originality/valueThere are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

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