Nhat-Duc Hoang 
- (2024): Enhancement of PZT-based damage detection in real-scale post-tensioned anchorage under ambient conditions. In: Journal of Constructional Steel Research, v. 220 (September 2024).
- (2022): Compressive Strength Estimation of Rice Husk Ash-Blended Concrete Using Deep Neural Network Regression with an Asymmetric Loss Function. In: Iranian Journal of Science and Technology, Transactions of Civil Engineering, v. 47, n. 3 (December 2022).
- (2022): Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network. In: Automation in Construction, v. 140 (August 2022).
- (2022): A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning. In: Advances in Civil Engineering, v. 2022 (January 2022).
- (2021): Concrete Spalling Severity Classification Using Image Texture Analysis and a Novel Jellyfish Search Optimized Machine Learning Approach. In: Advances in Civil Engineering, v. 2021 (January 2021).
- (2020): Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models. In: Advances in Civil Engineering, v. 2020 (January 2020).
- (2020): A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine. In: Advances in Civil Engineering, v. 2020 (January 2020).
- (2017): Fuzzy Clustering Chaotic-based Differential Evolution for Resource Leveling in Construction Projects. In: Journal of Civil Engineering and Management, v. 23, n. 1 (January 2017).
- (2015): Cash Flow Prediction for Construction Project Using A Novel Adaptive Time-dependent Least Squares Support Vector Machine Inference Model. In: Journal of Civil Engineering and Management, v. 21, n. 6 (June 2015).
- (2014): Groutability Prediction of Microfine Cement Based Soil Improvement Using Evolutionary Ls-svm Inference Model. In: Journal of Civil Engineering and Management, v. 20, n. 6 (December 2014).
- (2014): Interval Estimation of Construction Cost At Completion Using Least Squares Support Vector Machine. In: Journal of Civil Engineering and Management, v. 20, n. 2 (April 2014).
- (2019): Automatic detection of asphalt pavement raveling using image texture based feature extraction and stochastic gradient descent logistic regression. In: Automation in Construction, v. 105 (September 2019).
- (2018): Image Processing–Based Classification of Asphalt Pavement Cracks Using Support Vector Machine Optimized by Artificial Bee Colony. In: Journal of Computing in Civil Engineering, v. 32, n. 5 (September 2018).
- (2014): Groutability Estimation of Grouting Processes with Microfine Cements Using an Evolutionary Instance-Based Learning Approach. In: Journal of Computing in Civil Engineering, v. 28, n. 4 (July 2014).
- (2016): Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression. In: Journal of Computing in Civil Engineering, v. 30, n. 3 (May 2016).
- (2016): Predicting Colonization Growth of Algae on Mortar Surface with Artificial Neural Network. In: Journal of Computing in Civil Engineering, v. 30, n. 6 (November 2016).
- (2016): A Novel Relevance Vector Machine Classifier with Cuckoo Search Optimization for Spatial Prediction of Landslides. In: Journal of Computing in Civil Engineering, v. 30, n. 5 (September 2016).
- (2013): Hybrid intelligence approach based on LS-SVM and Differential Evolution for construction cost index estimation: A Taiwan case study. In: Automation in Construction, v. 35 (November 2013).
- (2014): A novel groutability estimation model for ground improvement projects in sandy silt soil based on Bayesian framework. In: Tunnelling and Underground Space Technology, v. 43 (July 2014).
- (2016): Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model. In: Advances in Civil Engineering, v. 2016 ( 2016).
- (2015): Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies. In: Advances in Civil Engineering, v. 2015 ( 2015).
- (2016): Estimating Concrete Workability Based on Slump Test with Least Squares Support Vector Regression. In: Journal of Construction Engineering, v. 2016 ( 2016).
- (2014): NIDE: A Novel Improved Differential Evolution for Construction Project Crashing Optimization. In: Journal of Construction Engineering, v. 2014 ( 2014).
- (2014): A Novel Resource-Leveling Approach for Construction Project Based on Differential Evolution. In: Journal of Construction Engineering, v. 2014 ( 2014).
- (2014): An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine. In: Journal of Construction Engineering, v. 2014 ( 2014).
- (2018): Fast Local Laplacian-Based Steerable and Sobel Filters Integrated with Adaptive Boosting Classification Tree for Automatic Recognition of Asphalt Pavement Cracks. In: Advances in Civil Engineering, v. 2018 ( 2018).
- (2018): An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extraction. In: Advances in Civil Engineering, v. 2018 ( 2018).
- (2018): Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding. In: Advances in Civil Engineering, v. 2018 ( 2018).
- (2018): Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms. In: Advances in Civil Engineering, v. 2018 ( 2018).
- (2018): Estimation of scour depth at bridges with complex pier foundations using support vector regression integrated with feature selection. In: Journal of Civil Structural Health Monitoring, v. 8, n. 3 (June 2018).
Structurae cooperates with
