AI in civil engineering最新文献

This review paper summarizes the current state of research on relationships between geotechnical soil’s properties and the California Bearing Ratio (CBR) value. Geotechnical elements are pivotal in preventing civil engineering projects from collapses and settlement failures, so understanding detailed soil properties is an important task. CBR tests are used to assess the stiffness modulus and shear strength and guide the overlaying layer’s thickness in pavement designs. Despite such tests’ high expense and complexity, researchers have explored correlations and machine learning for CBR prediction from soil properties. This paper would delve into the varying influence of such properties as compaction properties (OMC and MDD) and index properties (LL, PL, and PI). By measuring the relevance of these properties to CBR, this paper examines their significance and potential interactions. In sum, this review sheds light on soil properties’ multifaceted effects on CBR value and provides support for informed pavement engineering decisions.

Preserving the structural integrity of critical infrastructure systems necessitates a heightened focus on fortifying the protection of underground pipelines. To this end, this paper presents an innovative approach, namely the Multi-Sample Joint Localization Method (MSJLM) utilizing Cross-Correlation Convolutional Neural Networks (CC-CNN), aimed at precisely localizing intrusion sources in the vicinity of underground pipelines. Traditional techniques for detecting and pinpointing pipeline intrusions primarily rely on a single sensor monitoring point, which is susceptible to inherent errors and constraints. In contrast, the MSJLM proposed in this study leverages data from multiple samples, integrating diverse data sources through correlation analyses to elevate precision and reliability. The utilization of the CC-CNN framework for processing aggregated data has proven highly successful in extracting spatial features and identifying patterns. Furthermore, the effectiveness of this method is corroborated through validation via a pile-driving model test.

Optimization is the process of creating the best possible outcome while taking into consideration the given conditions. The ultimate goal of optimization is to maximize or minimize the desired effects to meet the technological and management requirements. When faced with a problem that has several possible solutions, an optimization technique is used to identify the best one. This involves checking different search domains at the right time, depending on the specific problem. To solve these optimization problems, nature-inspired algorithms are used as part of stochastic methods. In civil engineering, numerous design optimization problems are nonlinear and can be difficult to solve via traditional techniques. In such points, metaheuristic algorithms can be a more useful and practical option for civil engineering usages. These algorithms combine randomness and decisive paths to compare multiple solutions and select the most satisfactory one. This article briefly presents and discusses the application and efficiency of various metaheuristic algorithms in civil engineering topics.

The yearly production of plastic garbage is rising in the current environment as a result of the fast population rise. Recycling and reusing plastic trash is essential for sustainable development. The need of the hour is to utilize waste polythene for various supporting reasons since it is not biodegradable. These materials are made of polymers like polyethylene, polypropylene, and polystyrene. Due to the enhanced performance and elimination of the environmental issue, adding plastic waste to flexible pavement has emerged as a desirable choice. A composite material known as bituminous concrete (BC) is often utilized in construction projects such as road paving, airport terminals, and stopover areas. It includes mineral aggregate and black top or bitumen, which are combined, laid down in layers, and then compacted. The bituminous mixture in this research article was combined with plastic to use a chemical stabilizer. The ideal bitumen content is replaced by 0, 15%, 27%, and 36% plastic, as well as the bitumen's weight, stability, and Marshall value to create hypothermal. A linear scale is used to compare the flow rates to the bituminous mixture. The characterization of plastics contains bituminous materials are done by the SEM–EDX, XRD, FTIR and BET analysis. There have been several studies on the addition of trash to bituminous mixes, but this one is focused on the use of plastic waste as a modification in a bitumen binder for flexible pavement. According to research, bituminous mixes containing up to 4 percent plastic waste are excellent for sustainable development.

Earthquakes pose a significant threat to life and property worldwide. Rapid and accurate assessment of earthquake damage is crucial for effective disaster response efforts. This study investigates the feasibility of employing deep learning models for damage detection using drone imagery. We explore the adaptation of models like VGG16 for object detection through transfer learning and compare their performance to established object detection architectures like YOLOv8 (You Only Look Once) and Detectron2. Our evaluation, based on various metrics including mAP, mAP50, and recall, demonstrates the superior performance of YOLOv8 in detecting damaged buildings within drone imagery, particularly for cases with moderate bounding box overlap. This finding suggests its potential suitability for real-world applications due to the balance between accuracy and efficiency. Furthermore, to enhance real-world feasibility, we explore two strategies for enabling the simultaneous operation of multiple deep learning models for video processing: frame splitting and threading. In addition, we optimize model size and computational complexity to facilitate real-time processing on resource-constrained platforms, such as drones. This work contributes to the field of earthquake damage detection by (1) demonstrating the effectiveness of deep learning models, including adapted architectures, for damage detection from drone imagery, (2) highlighting the importance of evaluation metrics like mAP50 for tasks with moderate bounding box overlap requirements, and (3) proposing methods for ensemble model processing and model optimization to enhance real-world feasibility. The potential for real-time damage assessment using drone-based deep learning models offers significant advantages for disaster response by enabling rapid information gathering to support resource allocation, rescue efforts, and recovery operations in the aftermath of earthquakes.

Concrete is one of the most common construction materials used all over the world. Estimating the strength properties of concrete traditionally demands extensive laboratory experimentation. However, researchers have increasingly turned to predictive models to streamline this process. This review focuses on predicting the compressive strength of self-compacting concrete using artificial intelligence (AI) techniques. Self-compacting concrete represents an advanced construction material particularly suited for scenarios where traditional vibrational methods face limitations due to intricate formwork or reinforcement complexities. This review evaluates various AI techniques through a comparative performance analysis. The findings highlight that employing Deep Neural Network models with multiple hidden layers significantly enhances predictive accuracy. Specifically, artificial neural network (ANN) models exhibit robustness, consistently achieving R² values exceeding 0.7 across reviewed studies, thereby demonstrating their efficacy in predicting concrete compressive strength. The integration of ANN models is recommended for formulating various civil engineering properties requiring predictive capabilities. Notably, the adoption of AI models reduces both time and resource expenditures by obviating the need for extensive experimental testing, which can otherwise delay construction activities.

Quarry dust, conventionally considered waste, has emerged as a potential solution for sustainable construction materials. This paper comprehensively review the mechanical properties of blocks manufactured from quarry dust, with a particular focus on the transformative role of machine learning (ML) in predicting and optimizing these properties. By systematically reviewing existing literature and case studies, this paper evaluates the efficacy of ML methodologies, addressing challenges related to data quality, feature selection, and model optimization. It underscores how ML can enhance accuracy in predicting mechanical properties, providing a valuable tool for engineers and researchers to optimize the design and composition of blocks made from quarry dust. This synthesis of mechanical properties and ML applications contributes to advancing sustainable construction practices, offering insights into the future integration of technology for predictive modeling in material science.

Topology optimization techniques are increasingly utilized in structural design to create efficient and aesthetically pleasing structures while minimizing material usage. Many existing topology optimization methods may generate slender structural members under compression, leading to significant buckling issues. Consequently, incorporating buckling considerations is essential to ensure structural stability. This study investigates the capabilities of the bi-directional evolutionary structural optimization method, particularly its extension to handle multiple load cases in buckling optimization problems. The numerical examples presented focus on three classical cases relevant to civil engineering: maximizing the buckling load factor of a compressed column, performing buckling-constrained optimization of a frame structure, and enhancing the buckling resistance of a high-rise building. The findings demonstrate that the algorithm can significantly improve structural stability with only a marginal increase in compliance. The detailed mathematical modeling, sensitivity analyses, and optimization procedures discussed provide valuable insights and tools for engineers to design structures with enhanced stability and efficiency.

Small ground anchors are widely used to fix securing tents in disaster relief efforts. Given the urgent nature of rescue operations, it is crucial to obtain prompt and accurate estimations of their pullout capacity. In this study, a stacking machine learning (ML) model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents, leveraging cone penetration data. The proposed stacking model incorporates three ML algorithms as the base regression models: K-nearest neighbors (KNN), support vector regression (SVR), and extreme gradient boosting (XGBoost). A dataset comprising 119 in-situ anchor pullout tests, where the cone penetration data were measured, is utilized to train and assess the stacking model performance. Three metrics, i.e., coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE), are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach. The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R² and lower MAE and RMSE and more predicted data points falling within 20% error line. Thus, the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.

Machine learning is frequently used in various geotechnical applications nowadays. This study presents a statistics and machine learning model for settlement prediction of helical piles that relates compressive service load and soil parameters as a group with the pile parameters. Machine learning algorithms such as Decision Trees, Random Forests, AdaBoost, and Artificial Neural Networks (ANN) were used to develop the predictive models. The models were validated using cross-validation techniques and tested on an independent dataset to assess their accuracy and generalizability. Numerical investigation is used here to supplement the field data by simulating various soil conditions and pile geometries that have not been tested in the field. This study compiled numerical results of 3600 models. As the models are well-calibrated and validated, the data from these models can be reasonably assumed to simulate the ground situation. At the end of this study, a comparative analysis of statistic learning and machine learning (ML) was done using the field axial load tests database and numerical investigation on helical piles. It is observed that ML models like Decision Trees and Random Forests provided the better model with R-squared values of 0.92 and 0.96, respectively, for large diameters. The authors believe this study will permit engineers and state agencies to understand this prediction model's efficacy better, resulting in a more resilient approach to designing large-diameter helical piles for the compressive load.