This research aimed at applying revolutionary and transferable technologies, including the advanced sensing and 3D printing technologies, and artificial intelligence, to some of the problems in civil engineering. Three different applications, i.e., the determination of consolidation parameters, microstructural characterizations of soil samples and health monitoring of the civil infrastructures, were studied in this thesis.

In the first part, the U-oedometer, a novel modified oedometer cell equipped with tailor-made needle probes, is developed to easily and accurately measure the excess pore water pressure (Δ?) during 1-D consolidation tests, and to determine the coefficient of consolidation (?_?). The 3D printing technique is applied to make simple yet robust modifications to the...[ Read more ]

This research aimed at applying revolutionary and transferable technologies, including the advanced sensing and 3D printing technologies, and artificial intelligence, to some of the problems in civil engineering. Three different applications, i.e., the determination of consolidation parameters, microstructural characterizations of soil samples and health monitoring of the civil infrastructures, were studied in this thesis.

In the first part, the U-oedometer, a novel modified oedometer cell equipped with tailor-made needle probes, is developed to easily and accurately measure the excess pore water pressure (Δ?) during 1-D consolidation tests, and to determine the coefficient of consolidation (?_?). The 3D printing technique is applied to make simple yet robust modifications to the conventional oedometer cell for facilitating the installation of the needle probes. The tailor-made needle probes are designed in such a way that the volumetric compliance is lowered to avoid measurement bias. In addition, a power-regulating sensor board is used to stabilize the voltage output for long-term measurements so as to improve the accuracy of the Δ? measurements. Subsequently, the Δ? -based method is proposed to determine ?_?, through minimizing the difference between the measured and theoretical values of Δ? . Such an approach avoids the intervention of human judgement and therefore minimizes the degree of subjectivity. The experimental results demonstrate that the measured Δ? matches the theoretical values of the Terzaghi 1-D consolidation theory, showing that the estimated ?_? is sufficiently reliable to reflect the whole consolidation process. In addition to the determination of ?_?, the U-oedometer allows additional measurements of other soil properties during consolidation, including the coefficient of permeability (?) and the coefficient of earth pressure at rest (?₀). It is observed that k decreases with the reduction of void volume, due to the increase in effective vertical stress (?_?^′). Further, the secondary compression seems to be a continuation of the primary consolidation, where the soil sample continues to deform at a relatively slower rate, associated with the slight decrease in ?. A constant value of ?₀ is observed at any value of ?_?^′ in the loading path, while during secondary compression, ?₀ slightly increases with time.

The second part reports the use of the Deep Learning-based technique to rapidly, automatically and objectively characterize the microstructure of clay samples. The U-Net model was applied to perform semantic segmentation for identifying individual kaolinite particles, based on the Scanning Electron Microscopic (SEM) images taken from clay samples subjected to 1-D consolidation. To facilitate supervised learning, the elongated particles with known orientation were manually annotated first; additionally, data augmentation was extensively applied to increase the size of the training dataset and 5-fold cross-validation was used to ensure satisfactory generalization of the Deep Learning models on the unseen dataset. Dice loss and weighted cross-entropy were chosen as the loss functions to tackle the issue of imbalanced classification class. The customized weight maps incorporated in the weight cross-entropy were found effective in forcing the Deep Learning models to learn how to recognize the particle boundaries. With the trained Deep Learning models, the targeted kaolinite particles (>~12000 particles) were identified from the SEM images within ~20 mins and the particle directional distribution was quantified using the fabric tensor. The characterization results reveal that the kaolinite particles exhibit a tendency to gradually align along the horizontal plane as imposed by the applied vertical stress.

In the first section of the third part, a convolutional autoencoder was used to implement an unsupervised anomaly detection of the damage of concrete structures from images, so as to facilitate the condition assessment of the civil infrastructures. Upon the completion of training, this novel approach renders poor reconstruction of the defects of concrete structure, in turn, aids detecting the location of defects. Since the convolutional autoencoder was trained to reconstruct images, no label was required, thereby saving enormous time for annotating labels. Comparison was carried out with the segmentation results produced by other automatic classical methods. The analysis reveals that results made by the anomaly map generated by the proposed method outperform other segmentation methods, in terms of precision, recall, F₁ measure and F₂ measure, without severe under- and over-segmentation. Further, instead of merely being a binary map, each pixel of the anomaly map is represented by the anomaly score, which acts as risk indicator to alert inspectors, wherever the damage of concrete structures is detected.

In the second section of the third part, deep learning was explored to facilitate the health monitoring of the civil infrastructures, in terms of augmenting the degree of automation and minimizing the needs of labor-intensive processing works of the defect detection and classification. A classifier was first designed and trained from the scratch, for categorizing images to the classes of no defects, cracking and spalling. Upon the completion of the model training in a 5-fold cross-validation fashion, the built classifiers achieve satisfactory performance in categorizing images into the classes of no defects, cracking and spalling. Following that, an artificial intelligence-empowered monitoring pipeline was constructed. An unsupervised screening and detection of defects, made by the aforementioned convolutional autoencoder, was carried out in advance of the defect classification. This anomaly detector substantially reduces the search space of defects, in turn, allows more computational resources and time can be allocated on analyzing the defects of the concrete structures, which are the main focus of the task. Comprehensive analyses show that the proposed AI-empowered monitoring pipeline is capable and adaptable in detecting and classifying defects subjected to a wide variety of environmental conditions, including lighting condition, camera distance and capturing angle.