Artificial Intelligence (AI) Applied in Civil Engineering, 2nd Volume

This article presents a detailed examination of the methodology and modeling tools utilized to analyze gas flows in pipelines, rooted in the fundamental principles of gas dynamics. The methodology integrates numerical simulations with modern neural network techniques, particularly focusing on the PINN utilizing the continuous symmetry data inherent in PDEs, which is called the symmetry-enhanced Physics-Informed Neural Network. This innovative approach combines artificial neural networks (ANNs) integrating physical equations, which provide enhanced efficiency and accuracy when modeling various complex processes related to physics with a symmetric and asymmetric nature. The presented mathematical model, based on the system of Euler equations, has been carefully implemented using Python language. Verification with analytical solutions ensures the accuracy and reliability of the computations. In this research, a comparative and comprehensive analysis was carried out comparing the outcomes obtained using the symmetry-enhanced PINN method and those from conventional computational fluid dynamics (CFD) approaches. The analysis highlighted the advantages of the symmetry-enhanced PINN method, which produced smoother pressure and velocity fluctuation profiles while reducing the computation time, demonstrating its capacity as a revolutionary modeling tool. The estimated results derived from this study are of paramount importance for ensuring ongoing energy supply reliability and can also be used to create predictive models related to gas behavior in pipelines. The application of modeling techniques for gas flow simulations has the potential to improve the integrity of our energy infrastructure and utilization of gas resources, contributing to advancing our understanding of symmetry principles in nature. However, it is crucial to emphasize that the effectiveness of such models relies on continuous monitoring and frequent updates to ensure alignment with real-world conditions. This research not only contributes to a deeper understanding of compressible gas flows but also underscores the crucial role of advanced modeling methodologies in the sustainable management of gas resources for both current and future generations. The numerical data covered the physics of the process related to the modeling of high-pressure gas flows in pipelines with regard to density, velocity and pressure, where the PINN model was able to outperform the classical CFD method for velocity by 170% and for pressure by 360%, based on $L_{\infty }$ values. Full article

Due to the advantage that the non-convex penalty accurately characterizes the sparsity of structural damage, various models based on non-convex penalties have been effectively utilized to the field of structural damage identification. However, these models generally ignore the influence of the uncertainty on the damage identification, which inevitably reduces the accuracy of damage identification. To improve the damage identification accuracy, a probabilistic structural damage identification method with a generic non-convex penalty is proposed, where the uncertainty corresponding to each mode is quantified using the separate Gaussian distribution. The proposed model is estimated via the iteratively reweighted least squares optimization algorithm according to the maximum likelihood principle. The numerical and experimental results illustrate that the proposed method improves the damage identification accuracy by 3.98% and 7.25% compared to the original model, respectively. Full article

In this paper, an innovative method is put forward for estimating the dynamic mechanical behaviors of reinforced concrete (RC) column members by applying the random forest algorithm. Firstly, the development of dynamic modified coefficient (DMC) predictive models and the realization of the proposed method were elaborated. Then, due to the lack of dynamic loading tests on RC column members, a numerical model of RC columns considering the dynamic modification on flexural, shear and bond-slip behaviors was developed on the OpenSees platform, and the model accuracy and the effectiveness were verified with the available test results. Moreover, by comparing the simulated results of the hysteretic curve using numerical models with different complexities, the influences of dynamic modification and the deformation sub-element were investigated. Furthermore, a numerical experiment database was established to obtain the training data for developing the DMC predictive models of critical mechanical behavior parameters, including the yielding bearing capacity, ultimate bearing capacity and displacement ductility. Finally, the results of feature importance for different input parameters were studied, and the model accuracy was evaluated using the test set and available experimental data. It was revealed that the predictive models developed using the random forest algorithm can be employed to reliably estimate the dynamic mechanical behaviors of RC column members. Full article

In complex construction projects, technical specifications have to be reviewed in a short period of time. Even experienced engineers find it difficult to review every detail of technical specifications. In addition, it is not easy to transfer experienced knowledge to junior engineers. With the technological innovation of large language models such as ChatGPT, a fine-tuned language model is proposed as an effective solution for the automatic review of technical specification documents. Against this backdrop, this study examines the in-house technical specification documents that are not publicly available. Then, two fine-tuned large language models, GPT-3 and LLaMA2, are trained to answer questions related to technical specification documents. The results show that the fine-tuned LLaMA2 model generally outperforms the fine-tuned GPT-3 model in terms of accuracy, reliability, and conciseness of responses. In particular, the fine-tuned LLaMA2 model suppressed hallucinogenic effects better than the fine-tuned GPT-3 model. Based on the results, this study discussed the applicability and limitations of a fine-tuned large language model for in-house knowledge management. The results of this study are expected to assist practitioners in developing a domain-specific knowledge management solution by fine-tuning an open-source large language model with private datasets. Full article

Artificial neural networks (ANN) have a great promise in predicting the load-bearing capacity of building structures. The purpose of this work was to develop ANN models to determine the ultimate load of eccentrically compressed concrete-filled steel tubular (CFST) columns of circular cross-sections, which operated on the widest possible range of input parameters. Short columns were considered for which the amount of deflection does not affect the bending moment. A feedforward network was selected as the neural network type. The input parameters of the neural networks were the outer diameter of the columns, the thickness of the pipe wall, the yield strength of steel, the compressive strength of concrete and the relative eccentricity. Artificial neural networks were trained on synthetic data generated based on a theoretical model of the limit equilibrium of CFST columns. Two ANN models were created. When training the first model, the ultimate loads were determined at a given eccentricity of the axial force without taking into account additional random eccentricity. When training the second model, additional random eccentricity was taken into account. The total volume of the training dataset was 179,025 samples. Such a large training dataset size has never been used before. The training dataset covers a wide range of changes in the characteristics of the pipe metal and concrete of the core, pipe diameters and wall thicknesses, as well as eccentricities of the axial force. The trained models are characterized by high mean square error (MSE) scores. The correlation coefficients between the predicted and target values are very close to 1. The ANN models were tested on experimental data for 81 eccentrically compressed samples presented in five different works and 265 centrally compressed samples presented in twenty-six papers. Full article

This manuscript delineates an innovative artificial intelligence-based methodology for forecasting the displacement of retaining walls due to extensive deep excavation processes. In our selection of 17 training cases, we strategically chose a wall configuration that was not influenced by the corner effects. This careful selection was conducted with the intention of ensuring that each deep excavation instance included in our study was supported symmetrically, thereby streamlining the analysis in the ensuing phases. Our proposed multilayer functional-link network demonstrates superior performance over the traditional backpropagation neural network (BPNN), excelling in the precise prediction of displacements at predetermined observation points, peak wall displacements, and their respective locations. Notably, the predictive accuracy of our advanced model surpassed that of the conventional BPNN and RIDO assessment tools by a substantial 5%. The network process model formulated through this research offers a valuable reference for future implementations in diverse geographical settings. Furthermore, by utilizing local datasets for the training, testing, and validation phases, our system ensures the effective and accurate execution of displacement predictions. Full article

In recent years, the employment of artificial neural networks (ANNs) has risen in various engineering fields. ANNs have been applied to a range of geotechnical engineering problems and have shown promising outcomes. The aim of this article is to enhance the effectiveness of estimating unfamiliar intermediate values from existing shear stress data by employing ANNs. Artificial neural network modelling was undertaken through the Regression Learner program that is integrated with the Matlab 2023a software package. This program offers a user-friendly graphical interface for developing AI models absent of the need for any coding. The validation and training of the ANNs were executed by relying on shear box test data which had been conducted at the Geotechnical Laboratory situated at Iowa State University. The objective of these experiments was to explore the potential of biofuel co-products (BCPs) in soil stabilization. The data should be structured with input and output parameters in columns and samples in rows. The dataset comprises a 216 × 6 matrix. The data columns provide information on soil type (pure soil—unadulterated; and 12% BCP-adulterated soil), time (1, 7, and 28 days), normal stress (0.069-DS10, 0.138-DS20, and 0.207-DS30 MPa), moisture content (OMC−4%, OMC%, and OMC+4%), and corresponding shear stress (σ, MPa) values. The AI predictions for the test data output provide an outstanding R² score of 0.94. This indicates that employing ANN to teach shear test data facilitates gaining a large quantity of data more efficiently, with fewer experiments and in less time. Such an approach seems encouraging for geotechnical engineering. Full article

The utilization of Concrete-filled steel Tubular (CFST) columns is increasingly widespread. However, the assessment of the residual bearing capacity of CFST columns currently relies mainly on costly and time-consuming experiments and numerical simulations. In this study, we propose a machine learning-based model for rapidly identifying the residual bearing capacity of CFST columns. The results demonstrate that the predictions of the proposed Stacking-KRXL model align well with the actual values, with most prediction errors falling within ±10%. The RSquared value of 0.97 significantly surpasses that of other methods. The stability and robustness of the model are analyzed. Additionally, the Shapley additive explanations method is applied for global and local interpretations, revealing positive or negative correlations between different parameters and the residual bearing capacity of CFST columns, mainly influenced by the concrete area in the core region. Full article

The deformation of concrete-face rockfill dams (CFRDs) is a key parameter for the safety control of reservoir and dam systems. Rapid and accurate estimation of the deformation characteristics of CFRDs is a top priority. To realize this, we proposed a new model for predicting the maximum face slab deflection (FD) of CFRDs, combining the threshold regression (TR) and the improved support vector machine (SVM). In this paper, based on the collected 71 real measurement data from engineering examples, we constructed an adaptive hybrid kernel function with high precision and generalization ability. We optimized the selection of the main parameters of the SVM by a particle swarm optimization (PSO) algorithm. Meanwhile, we clustered the deformation parameters according to the dam height by the TR. It significantly contributes to the accuracy and generalization of the model. Finally, a prediction model for the FD characteristics of CFRDs combining TR and improved SVM was developed. The new prediction model can overcome the nonlinear abrupt feature of the sample data and achieve high precision with R² greater than 0.8 in the final testing set. Our model is more accurate with faster convergence compared to the previous model. This study provides a more accurate model for predicting maximum face slab deflection and lays the foundation for safety control and evaluation of dams. Full article

Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. Point cloud semantic segmentation (PCSS) enables the automatic extraction of semantic information from 3D point cloud data, which makes it a desirable task for construction-related applications as well. Yet, only a limited number of publications have applied deep-learning-based methods to address point cloud understanding for civil engineering problems, and there is still a lack of comprehensive reviews and evaluations of PCSS methods tailored to such use cases. This paper aims to address this gap by providing a survey of recent advances in deep-learning-based PCSS methods and relating them to the challenges of the construction industry. We introduce its significance for the industry and provide a comprehensive look-up table of publicly available datasets for point cloud understanding, with evaluations based on data scene type, sensors, and point features. We address the problem of class imbalance in 3D data for machine learning, provide a compendium of commonly used evaluation metrics for PCSS, and summarize the most significant deep learning methods developed for PCSS. Finally, we discuss the advantages and disadvantages of the methods for specific industry challenges. Our contribution, to the best of our knowledge, is the first survey paper that comprehensively covers deep-learning-based methods for semantic segmentation tasks tailored to construction applications. This paper serves as a useful reference for prospective research and practitioners seeking to develop more accurate and efficient PCSS methods. Full article

This paper proposes an approach that utilizes Artificial-Intelligence-Generated Content (AIGC) to overcome the constraints of Structural Health Monitoring (SHM) devices in capturing global stress with limited sensors. Feature elements are selected based on correlation analysis among finite elements and used as stress-measured points. An Artificial Neural Network (ANN) is used to establish the relationship between the feature and correlation elements. The proposed method is applied to the connector structure of an offshore platform, and an optimal ANN is established to optimize its performance by considering factors such as the number of sensors, the neural network framework, and the convergence criteria. The generalization performance of the ANN is validated through a real-scale model test, with deviations below 10% and an average deviation of less than 4% in multiple conditions, verifying its accuracy. This technology represents a significant advancement, enhancing the practicality of the SHM technology from “point monitoring” to “field monitoring”. Full article