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26 pages, 25984 KiB

Open AccessArticle

A Framework for Using UAVs to Detect Pavement Damage Based on Optimal Path Planning and Image Splicing

by Runmin Zhao, Yi Huang, Haoyuan Luo, Xiaoming Huang and Yangzezhi Zheng

Sustainability 2023, 15(3), 2182; https://0-doi-org.brum.beds.ac.uk/10.3390/su15032182 - 24 Jan 2023

Cited by 2 | Viewed by 1596

In order to investigate the use of unmanned aerial vehicles (UAVs) for future application in road damage detection and to provide a theoretical and technical basis for UAV road damage detection, this paper determined the recommended flight and camera parameters based on the [...] Read more.

In order to investigate the use of unmanned aerial vehicles (UAVs) for future application in road damage detection and to provide a theoretical and technical basis for UAV road damage detection, this paper determined the recommended flight and camera parameters based on the needs of continuous road image capture and pavement disease recognition. Furthermore, to realize automatic route planning and control, continuous photography control, and image stitching and smoothing tasks, a UAV control framework for road damage detection, based on the Dijkstra algorithm, the speeded-up robust features (SURF) algorithm, the random sampling consistency (RANSAC) algorithm, and the gradual in and out weight fusion method, was also proposed in this paper. With the Canny operator, it was verified that the road stitched long image obtained by the UAV control method proposed in this paper is applicable to machine learning pavement disease identification. Full article

(This article belongs to the Special Issue Construction Structure Health Monitoring for Sustainable Built Environment)

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15 pages, 2090 KiB

Open AccessArticle

Random Forest Algorithm for the Strength Prediction of Geopolymer Stabilized Clayey Soil

by Husein Ali Zeini, Duaa Al-Jeznawi, Hamza Imran, Luís Filipe Almeida Bernardo, Zainab Al-Khafaji and Krzysztof Adam Ostrowski

Sustainability 2023, 15(2), 1408; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021408 - 11 Jan 2023

Cited by 13 | Viewed by 1955

Abstract

Unconfined compressive strength (UCS) can be used to assess the applicability of geopolymer binders as ecologically friendly materials for geotechnical projects. Furthermore, soft computing technologies are necessary since experimental research is often challenging, expensive, and time-consuming. This article discusses the feasibility and the [...] Read more.

Unconfined compressive strength (UCS) can be used to assess the applicability of geopolymer binders as ecologically friendly materials for geotechnical projects. Furthermore, soft computing technologies are necessary since experimental research is often challenging, expensive, and time-consuming. This article discusses the feasibility and the performance required to predict UCS using a Random Forest (RF) algorithm. The alkali activator studied was sodium hydroxide solution, and the considered geopolymer source material was ground-granulated blast-furnace slag and fly ash. A database with 283 clayey soil samples stabilized with geopolymer was considered to determine the UCS. The database was split into two sections for the development of the RF model: the training data set (80%) and the testing data set (20%). Several measures, including coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE), were used to assess the effectiveness of the RF model. The statistical findings of this study demonstrated that the RF is a reliable model for predicting the UCS value of geopolymer-stabilized clayey soil. Furthermore, based on the obtained values of RMSE = 0.9815 and R² = 0.9757 for the testing set, respectively, the RF approach showed to provide excellent results for predicting unknown data within the ranges of examined parameters. Finally, the SHapley Additive exPlanations (SHAP) analysis was implemented to identify the most influential inputs and to quantify their behavior of input variables on the UCS. Full article

(This article belongs to the Special Issue Construction Structure Health Monitoring for Sustainable Built Environment)

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22 pages, 6753 KiB

Open AccessArticle

Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

by Aman Kumar, Harish Chandra Arora, Nishant Raj Kapoor, Mazin Abed Mohammed, Krishna Kumar, Arnab Majumdar and Orawit Thinnukool

Sustainability 2022, 14(4), 2404; https://0-doi-org.brum.beds.ac.uk/10.3390/su14042404 - 19 Feb 2022

Cited by 49 | Viewed by 5086

Abstract

Concrete is the most commonly used construction material. The physical properties of concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre-reinforced concrete, polymer-modified concrete, and lightweight concrete. The precise prediction of the properties of concrete is a problem [...] Read more.

Concrete is the most commonly used construction material. The physical properties of concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre-reinforced concrete, polymer-modified concrete, and lightweight concrete. The precise prediction of the properties of concrete is a problem due to the design code, which typically requires specific characteristics. The emergence of a new category of technology has motivated researchers to develop mechanical strength prediction models using Artificial Intelligence (AI). Empirical and statistical models have been extensively used. These models require a huge amount of laboratory data and still provide inaccurate results. Sometimes, these models cannot predict the properties of concrete due to complexity in the concrete mix design and curing conditions. To conquer such issues, AI models have been introduced as another approach for predicting the compressive strength and other properties of concrete. This article discusses machine learning algorithms, such as Gaussian Progress Regression (GPR), Support Vector Machine Regression (SVMR), Ensemble Learning (EL), and optimized GPR, SVMR, and EL, to predict the compressive strength of Lightweight Concrete (LWC). The simulation approaches of these trained models indicate that AI can provide accurate prediction models without undertaking extensive laboratory trials. Each model’s applicability and performance were rigorously reviewed and assessed. The findings revealed that the optimized GPR model (R = 0.9803) used in this study had the greatest accuracy. In addition, the optimized SVMR and GPR model showed good performance, with R-values 0.9777 and 0.9740, respectively. The proposed model is economic and efficient, and can be adopted by researchers and engineers to predict the compressive strength of LWC. Full article

(This article belongs to the Special Issue Construction Structure Health Monitoring for Sustainable Built Environment)

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