AI in civil engineering最新文献

With rapid advances in design methods and structural analysis techniques, computational generative design strategies have been adopted more widely in the field of architecture and engineering. As a performance-based design technique to find out the most efficient structural form, topology optimization provides a powerful tool for designers to explore lightweight and elegant structures. Building on this background, this study proposes an innovative pedestrian bridge design, which covers the process from conceptualization to detailed design implementation. This pedestrian bridge, with a main span of 152 m, needs to meet some unique architectural requirements, while addressing multiple engineering challenges. Aiming to reduce the depth of the girder but still meeting the load-carrying capacity requirements, the superstructure of this bridge adopts a variable-depth spinal-shaped girder in the center of its deck, thus forming an elegant curving facade, from which one pathway cantilevers on either side. At one end of the bridge, given considerable elevation difference between the bridge deck and the ground, a two-level Fibonacci-type spiral-shaped bicycle ramp is provided. The superstructure is supported by a series of organic tree-shaped branching piers resulting from the topology optimization. The ingenious design for the elegant profile of the bicycle ramp generates an enjoyable and dynamic crossing experience, with scenic views in all directions. By virtue of technological innovation, the pedestrian bridge is expected to create an iconic, cost-effective, and low-maintenance solution. A brief overview of the theoretical background of the bi-directional evolutionary structure optimization (BESO) and the multi-material BESO approach is also offered in this paper, while the construction requirements and challenges, conceptual development process, form-finding strategy, detailed design, and construction method of the bridge are presented.

Auxetic honeycomb sandwich structures (AHS) composed of a single material generally exhibit comparatively lower energy absorption (EA) and platform stress, as compared to traditional non-auxetic sandwich structures (TNS). To address this limitation, the present study examines the use of aluminum foam (AF) as a filling material in the re-entrant honeycomb sandwich structure (RS). Filling the AHS with AF greatly enhances both the EA and platform stress in comparison to filling the TNS with AF, while the auxetic composite honeycomb sandwich structure effectively addresses interface delamination observed in traditional non-auxetic composite sandwich structures. Subsequently, the positive–negative Poisson’s ratio coupling designs are proposed to strengthen the mechanical features of a single honeycomb sandwich structure. The analysis results show that the coupling structure optimizes the mechanical properties by leveraging the high bearing capacity of the hexagonal honeycomb and the great interaction between the re-entrant honeycomb and the filling material. In contrast with traditional non-auxetic sandwich structures, the proposed auxetic composite honeycomb sandwich structures demonstrate superior EA and platform stress performance, suggesting their immense potential for utilization in protective engineering.

The construction industry relies so heavily on concrete that it's crucial to precisely forecast and optimize the strength and workability of concrete mixtures, while reducing costs as much as possible. For this objective, this study tries to predict and optimize the compressive strength and workability (slump) of concrete by using deterministic and robust optimization approaches, so as to determine the optimum concrete mixture proportions, while minimizing cost. Specifically, strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine (SVM), artificial neural network (ANN), fuzzy inference system (FIS), adaptive fuzzy inference system (ANIS), and genetic expression programming (GEP), based on a dataset comprising two hundred concrete mixtures, which has various levels of key ingredients, including cement, water, fine aggregate, coarse aggregate, and size of coarse aggregate, along with their associated measures of strength and workability. These ingredients were used as input parameters, while compressive strength and slump (representing workability) served as output parameters for each mix proportion. Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks, finding that ANFIS can yield the best results both for training and validation. This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions, thus helping to maximize strength and workability while minimizing costs.

The presence of Artificial Intelligence (AI) and Machine Learning (ML) applications has led to its widespread adoption across diverse domains. AI is making its way into industry, beyond research and academia. Concurrently, the water sector is undergoing a digital transformation. Water utilities in the United States are at different stages in their journey of digital transformation, and the decision makers in water sector, who are non-expert stakeholders in AI applications, need to better understand this technology to make informed decisions. While AI has numerous benefits to offer, there are also many challenges related to data, model development, knowledge integration and ethical concerns that should be considered before implementing it for real world applications. Civil engineering is a licensed profession where critical decision making is involved. Therefore, trust in any decision support technology is critical for its acceptance in real-world applications. Therefore, this research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) which can serve as a guide for the water utilities to successfully implement AI in their system. Based on this framework, we conduct pilot interviews and surveys with various small, medium, and large water utilities in the United States (US) to capture their current state of AI implementation and identify the challenges faced by them. The research findings reveal that most of the water utilities in the United States are at an early stage of implementing AI as they face concerns regarding the black box nature, trustworthiness, and sustainability of AI technology in their system. The aiWATERS framework is intended to help the utilities navigate through these issues in their journey of digital transformation.

In light of rising loads from several sources, including additional stories, eccentric loads, and increased live loads, foundations often face increased demands. To address this, horizontal reinforcements are now commonly positioned beneath footings to enhance the bearing capacity of the loose-dense sand subgrade. By grouting on both sides of the footing, not only can vertical settlement be minimized, but also the soil movement in the horizontal direction under the chosen loaded footing can be reduced. The objective of this study is to conduct extensive experimental work on twenty-one (21) soil models to assess the efficiency of a circular footing resting on granular soil injected with grout diaphragm walls. Specifically, this study investigated the bearing capacity of granular soil in relation to the breadth (b) and length (L) of grouted walls. The results showed that, installing grouted wall injection on both sides of the existing footing is an excellent method to improve the bearing capacity of the subgrade layer. To check the validity of the chosen computational processes, both PLAXIS (3D) software and a 2D Finite Element Program GeoStudio 2018 were used. The findings indicate a direct correlation with the experimental observations in that the reinforcement has a considerable effect on the bearing capacity of a circular-footing resting on granular soil.

Underground infrastructure plays a kind of crucial role in modern production and living, especially in big cities where the ground space has been fully utilized. In the context of recent advancements in digital technology, the demand for the application of digital twin technology in underground infrastructure has become increasingly urgent as well. However, the interaction and co-integration between underground engineering entities and virtual models remain relatively limited, primarily due to the unique nature of underground engineering data and the constraints imposed by the development of information technology. This research focuses on underground engineering infrastructure and provides an overview of the application of novel information technologies. Furthermore, a comprehensive framework for digital twin implementation, which encompasses five dimensions and combines emerging technologies, has been proposed. It thereby expands the horizons of the intersection between underground engineering and digital twins. Additionally, a practical project in Wenzhou serves as a case study, where a comprehensive database covering the project’s entire life cycle has been established. The physical model is visualized, endowed with functional implications and data analysis capabilities, and integrated with the visualization platform to enable dynamic operation and maintenance management of the project.

In order to rapidly and accurately evaluate the mechanical properties of a novel origami-inspired tube structure with multiple parameter inputs, this study developed a method of designing origami-inspired braces based on machine learning models. Four geometric parameters, i.e., cross-sectional side length, plate thickness, crease weakening coefficient, and plane angles, were used to establish a mapping relationship with five mechanical parameters, including elastic stiffness, yield load, yield displacement, ultimate load, and ultimate displacement, all of which were calculated from load-displacement curves. Firstly, forward prediction models were trained and compared for single and multiple mechanical outputs. The parameter ranges were extended and refined to improve the predicted results by introducing the intrinsic mechanical relationships. Secondly, certain reverse prediction models were established to obtain the optimized design parameters. Finally, the design method of this study was verified in finite element methods. The design and analysis framework proposed in this study can be used to promote the application of other novel multi-parameter structures.

Computer programming-based numerical programs are firmly established in geotechnical engineering, with rapid growth of finite element modeling and machine learning techniques gaining much attention both in practice and academia. This study is intended to expedite the dissemination of advanced computer applications in terms of finite element simulation and machine learning models by investigating the dynamic response of geomaterials subjected to vibratory loads. Several trial models were built to perform the experimental investigations with a vibratory shaker, signal generator, several accelerometers, a data collection system, and other ancillary devices. The implicit integration techniques in commercialized software were adopted for numerical simulations. After data collection from numerical simulation, models were chosen, trained, and assessed to produce predictions that were then used in this study. Several technologies, including the ensemble boosted tree, squared exponential Gaussian Process Regression (GPR), Matern 5/2 GPR, exponential GPR, and decision tree architectures (fine and medium), were used to forecast the displacement of confined geomaterial. The displacement-depth ratio was found rising to 80% in the frequency range of 5 to 25 Hz, suggesting a considerable change in the behavior of the geomaterial. The Matern 5/2 GPR model showed better accuracy with an R² value of 0.99, indicating an outstanding predictive ability. The Matern 5/2 GPR and boosted tree models could help better understand the links between displacement and its distribution along the direction of load application. The outcomes of this study based on computer-aided finite element programs can be effectively implemented in machine learning to develop computer programs. In conclusion, the computational machine learning models adopted in this study offer a new insight for uncovering hidden intrinsic laws and creating new knowledge for geotechnical researchers and practitioners.

The study of drainage behavior is essential for using waste material in geotechnical applications. In this study, sandy soil was replaced with waste foundry sand (WFS) at an incremental interval of 20% by weight. Permeability (k) for each mix was acquired at three relative densities (R_D), i.e., 65%, 75% and 85%, by using the constant head method. Then the results were further processed with machine learning (ML) models to validate the experimental data. The experimental study demonstrated that k would decrease with the increase in relative density and WFS content. A rise in R_D from 65% to 85% resulted in a substantial reduction of up to 140% in the value of k. Moreover, the complete replacement of sand with WFS reduced the value of k by 36%, 51% and 57% for R_D of 65%, 75% and 85%, respectively. The total dataset of 90 observations was divided at a ratio of 63/13/15 into training/validation/testing datasets for ML-AI modeling. Input variables include percentage of sand (BS), replacement with WFS, total head (H), time interval (t) and outflow (Q); and k is the output variable. The methods of artificial neural network (ANN), random forest (RF), decision tree (DT) and multi-linear regression (MLR) are used for k prediction. It is found that the random forest approach performed outstandingly in these methods, with an R² value of 0.9955. The performance of all the proposed methods was compared and verified with Taylor's diagram. Sensitivity analysis showed that Q and R_D were the most influential parameters for predicting k values.

The use of deep generative models (DGMs) such as variational autoencoders, autoregressive models, flow-based models, energy-based models, generative adversarial networks, and diffusion models has been advantageous in various disciplines due to their high data generative skills. Using DGMs has become one of the most trending research topics in Artificial Intelligence in recent years. On the other hand, the research and development endeavors in the civil structural health monitoring (SHM) area have also been very progressive owing to the increasing use of Machine Learning techniques. As such, some of the DGMs have also been used in the civil SHM field lately. This short review communication paper aims to assist researchers in the civil SHM field in understanding the fundamentals of DGMs and, consequently, to help initiate their use for current and possible future engineering applications. On this basis, this study briefly introduces the concept and mechanism of different DGMs in a comparative fashion. While preparing this short review communication, it was observed that some DGMs had not been utilized or exploited fully in the SHM area. Accordingly, some representative studies presented in the civil SHM field that use DGMs are briefly overviewed. The study also presents a short comparative discussion on DGMs, their link to the SHM, and research directions.