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In recent years, the use of recycled aggregate (RA) in roller-compacted concrete (RCC) for pavement construction has been increasingly attractive due to various environmental and economic benefits. Early determination of the compressive strength (CS) is crucial for the construction and maintenance of pavement. This paper presents the idea of combining metaheuristics and an advanced gradient boosting regressor for estimating the compressive strength of roller-compacted concrete containing RA. A dataset, including 270 samples, has been collected from previous experimental works. Recycled aggregates of construction demolition waste, reclaimed asphalt pavement, and industrial slag waste are considered in this dataset. The extreme gradient boosting machine (XGBoost) is employed to generalize a functional mapping between the CS and its influencing factors. A recently proposed gradient-based optimizer (GBO) is used to fine-tune the training phase of XGBoost in a data-driven manner. Experimental results show that the hybrid GBO-XGBoost model achieves outstanding prediction accuracy with a root mean square error of 2.64 and a mean absolute percentage error less than 8%. The proposed method is capable of explaining up to 94% of the variation in the CS. Additionally, an asymmetric loss function is implemented with GBO-XGBoost to mitigate the overestimation of CS values. It was found that the proposed model trained with the asymmetric loss function helped reduce overestimated cases by 17%. Hence, the newly developed GBO-XGBoost can be a robust and reliable approach for predicting the CS of RCC using RA. [ABSTRACT FROM AUTHOR]

Structural damage in the steel bridge anchorage, if not diagnosed early, could pose a severe risk of structural collapse. Previous studies have mainly focused on diagnosing prestress loss as a specific type of damage. This study is among the first for the automated identification of multiple types of anchorage damage, including strand damage and bearing plate damage, using deep learning combined with the EMA (electromechanical admittance) technique. The proposed approach employs the 1D CNN (one-dimensional convolutional neural network) algorithm to autonomously learn optimal features from the raw EMA data without complex transformations. The proposed approach is validated using the raw EMA response of a steel bridge anchorage specimen, which contains substantial nonlinearities in damage characteristics. A K-fold cross-validation approach is used to secure a rigorous performance evaluation and generalization across different scenarios. The method demonstrates superior performance compared to established 1D CNN models in assessing multiple damage types in the anchorage specimen, offering a potential alternative paradigm for data-driven damage identification in steel bridge anchorages. [ABSTRACT FROM AUTHOR]

This study proposes a novel integration of the Extreme Gradient Boosting Machine (XGBoost) and Differential Flower Pollination (DFP) for constructing an intelligent method to predict the compressive strength (CS) of high-performance concrete (HPC) mixes. The former is employed to generalize a mapping function between the mechanical property of concrete and its influencing factors. DFP, as a metaheuristic algorithm, is employed to optimize the learning phase of XGBoost and reach a fine balance between the two goals of model building: reducing the prediction error and maximizing the generalization capability. To construct the proposed method, a historical dataset consisting of 400 samples was collected from previous studies. The model's performance is reliably assessed via multiple experiments and Wilcoxon signed-rank tests. The hybrid DFP-XGBoost is able to achieve good predictive outcomes with a root mean square error of 5.27, a mean absolute percentage error of 6.74%, and a coefficient of determination of 0.94. Additionally, quantile regression based on XGBoost is performed to construct interval predictions of the CS of HPC. Notably, an asymmetric error loss is used to diminish overestimations committed by the model. It was found that this loss function successfully reduced the percentage of overestimated CS values from 47.1% to 27.5%. Hence, DFP-XGBoost can be a promising approach for accurately and reliably estimating the CS of untested HPC mixes. [ABSTRACT FROM AUTHOR]

This study proposes a data-driven method for predicting the compressive strength (CS) of roller-compacted concrete (RCC). The new method is an integration of light gradient boosting machine (LightGBM) and particle swarm optimization (PSO). The former is used to generalize the mapping relationship between the CS and its influencing factors. The latter is employed to optimize the learning phase of LightGBM. A dataset, consisting of 408 laboratory tests and eight predictor variables, has been collected from the literature to train and verify the proposed data-driven method. Experimental results, supported by statistical hypothesis tests, point out that the proposed approach achieves outstanding performance with a root mean squared error of 4.53 and a mean absolute percentage error of 13.2%. The new model, named PSO-LightGBM, is capable of explaining roughly 90% of the variation in the CS of RCC mixes. In addition, an asymmetric loss function is applied in the training phase of LightGBM to reduce the proportion of overestimated CS values from 53% to 32%. This reduction in overestimations can help enhance the reliability of the data-driven method. Therefore, the newly developed model can serve as a decision-supporting tool in designing RCC mixtures. [ABSTRACT FROM AUTHOR]

Fatigue failure is a major structural defect found in the asphalt pavement subjected to repeated traffic loadings. In order to establish cost-effective maintenance plans, timely detection of pavement fatigue and classification of its severity are crucial. This study aims at developing an advanced image processing method based on Gaussian steerable filters, projection integrals, and texture descriptors for automating the tasks of interest. The extremely randomized trees (ERT) and deep neural network (DNN) are used to analyze the features extracted from the aforementioned image processing methods. The performance of ERT and DNN is also benchmarked against that of the convolutional neural network. A dataset consisting of 6000 samples has been collected in Da Nang city (Vietnam) to construct and verify the proposed computer vision approaches. Experimental results supported by Wilcoxon signed-rank tests confirm that the ERT-based method has achieved the most desired classification performance with an accuracy rate > 0.93. [ABSTRACT FROM AUTHOR]