Artificial Intelligence (AI) Applied in Civil Engineering

Lagaros, Nikos D.; Plevris, Vagelis

doi:10.3390/app12157595

Cited by 21 publications

(10 citation statements)

References 41 publications

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“…It allows the potential to utilise state-of-the-art environmentally friendly materials, such as biodegradable, biocompatible, and natural polymers, and to produce sophisticated structures and architectural models with a lower cost and higher efficiency [18]. As a supplementary technology, 3D printing's function and effectiveness should rely on the degree of integration with computational modelling or algorithmic design, so that a good combination and coordination of technologies, such as BIM and AI, will be critical to actualise its potential [19,20]. Modular construction as an offsite production system is another method used to achieve less resource wastage.…”

Section: Minimising Waste and Negative Environmental Impactsmentioning

confidence: 99%

Technology Innovation for Sustainability in the Building Construction Industry: An Analysis of Patents from the Yangtze River Delta, China

Wang

Zhang

2022

Buildings

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Advances in technology provides the potential to innovate sustainability in the building construction industry. Drawing on the literature and expert reviews, this research discloses the potential of 14 specific technologies in waste minimisation, energy saving, and efficiency improvement for sustainable building construction and develops a conceptual framework engaging the building life cycle (the planning, construction, use and operation, and demolition phases) and the actors for assessing technological innovation at a local level. This framework is used to identify how technologies were innovated for sustainable building construction through the selected 3017 patent invention applications from the Yangtze River Delta (YRD), one of the fastest urbanising areas and the largest market for the building industry in China. Findings unveiled that innovation is uneven amongst the 14 technologies and the degree of mixing was relatively low in the YRD. The contribution of the technological innovation to sustainability is mainly actualised through efficiency improvements in building construction (2265) and through directly reducing waste (1094) and energy consumption (642). Some general-purpose technologies (e.g., blockchain, cloud computing), which assume to fundamentally innovate the industry, are mostly absent with less than 10 records each, leaving the potential for future adaptive technological innovation. Furthermore, state-owned enterprises as the main sources of patent inventions amongst applicants in the YRD may suggest the dissimilar path of China towards technology innovation compared to its Anglo-American counterparts.

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Section: Minimising Waste and Negative Environmental Impactsmentioning

confidence: 99%

Technology Innovation for Sustainability in the Building Construction Industry: An Analysis of Patents from the Yangtze River Delta, China

Wang

Zhang

2022

Buildings

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“…Methodologies based on artificial intelligence (AI) are becoming increasingly common nowadays, with the increase in available computational power and the continuous development of techniques that are constantly improving prediction methods. For example, a large variety of such methodologies are applied in civil engineering for the prediction of structural response; see [1][2][3]. One of the most common machine learning algorithms that are used for the prediction of structural response is the regression artificial neural network (ANN).…”

Section: Introductionmentioning

confidence: 99%

Stepwise Regression for Increasing the Predictive Accuracy of Artificial Neural Networks: Applications in Benchmark and Advanced Problems

Papazafeiropoulos

2024

Modelling

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A new technique is proposed to increase the prediction accuracy of artificial neural networks (ANNs). This technique applies a stepwise regression (SR) procedure to the input data variables, which adds nonlinear terms into the input data in a way that maximizes the regression between the output and the input data. In this study, the SR procedure adds quadratic terms and products of the input variables on pairs. Afterwards, the ANN is trained based on the enhanced input data obtained by SR. After testing the proposed SR-ANN algorithm in four benchmark function approximation problems found in the literature, six examples of multivariate training data are considered, of two different sizes (big and small) often encountered in engineering applications and of three different distributions in which the diversity and correlation of the data are varied, and the testing performance of the ANN for varying sizes of its hidden layer is investigated. It is shown that the proposed SR-ANN algorithm can reduce the prediction error by a factor of up to 26 and increase the regression coefficient between predicted and actual data in all cases compared to ANNs trained with ordinary algorithms.

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“…In fact, the growing interest in the utilization of AI tools within these fields is well documented in the existing literature. For example, Huang et al [11], Lagaros and Plevris [12], Manzoor et al [13], Dede et al [14], Pan and Zhang [15], and Lu et al [16] reviewed the application of AI tools in civil engineering, while Momade et al [17] and Bölek et al [18] documented the recent applications of these tools in architecture.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

Lozano,

Emadi,

Komarizadehasl

et al. 2024

Buildings

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The development of low-cost structural and environmental sensors has sparked a transformation across numerous fields, offering cost-effective solutions for monitoring infrastructures and buildings. However, the affordability of these solutions often comes at the expense of accuracy. To enhance precision, the LARA (Low-cost Adaptable Reliable Anglemeter) system averaged the measurements of a set of five different accelerometers working as inclinometers. However, it is worth noting that LARA’s sensitivity still falls considerably short of that achieved by other high-accuracy commercial solutions. There are no works presented in the literature to enhance the accuracy, precision, and resolution of low-cost inclinometers using artificial intelligence (AI) tools for measuring structural deformation. To fill these gaps, artificial intelligence (AI) techniques are used to elevate the precision of the LARA system working as an inclinometer. The proposed AI-driven tool uses Multilayer Perceptron (MLP) to glean insight from high-accuracy devices’ responses. The efficacy and practicality of the proposed tools are substantiated through the structural and environmental monitoring of a real steel frame located in Cuenca, Spain.

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Artificial Intelligence (AI) Applied in Civil Engineering

Abstract: In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis [...]

Cited by 21 publications

References 41 publications

Technology Innovation for Sustainability in the Building Construction Industry: An Analysis of Patents from the Yangtze River Delta, China

Technology Innovation for Sustainability in the Building Construction Industry: An Analysis of Patents from the Yangtze River Delta, China

Stepwise Regression for Increasing the Predictive Accuracy of Artificial Neural Networks: Applications in Benchmark and Advanced Problems

Enhancing the Accuracy of Low-Cost Inclinometers with Artificial Intelligence

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