Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review

Manzoor, Bilal; Othman, Idris; Durdyev, Serdar; Ismail, Shahrinaz; Wahab, Mohammad Hussaini

doi:10.3390/asi4030052

Cited by 59 publications

(35 citation statements)

References 135 publications

(131 reference statements)

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“…The logistic regression followed by CNN infers the probability P that there is an expansion joint in the image patch. The error of the model prediction for each image patch is calculated with binary cross entropy [64][65][66][67][68][69], which compares each of the predictions to the actual class, which can be either 0 or 1. The errors of each set of training data are summed up.…”

Section: Expansion Joint Device Recognitionmentioning

confidence: 99%

Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images

Kim

Cho

et al. 2021

ASI

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Recently, the lack of expansion joint gaps on highway bridges in Korea has been increasing. In particular, with the increase in the number of days during the summer heatwave, the narrowing of the expansion joint gap causes symptoms such as expansion joint damage and pavement blow-up, which threaten traffic safety and structural safety. Therefore, in this study, we developed a machine vision (M/V)-technique-based inspection system that can monitor the expansion joint gap through image analysis while driving at high speed (100 km/h), replacing the current manual method that uses an inspector to inspect the expansion joint gap. To fix the error factors of image analysis that happened during the trial application, a machine learning method was used to improve the accuracy of measuring the gap between the expansion joint device. As a result, the expansion gap identification accuracy was improved by 27.5%, from 67.5% to 95.0%, and the use of the system reduces the survey time by more than 95%, from an average of approximately 1 h/bridge (existing manual inspection method) to approximately 3 min/bridge. We assume, in the future, maintenance practitioners can contribute to preventive maintenance that prepares countermeasures before problems occur.

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Section: Expansion Joint Device Recognitionmentioning

confidence: 99%

Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images

Kim

Cho

et al. 2021

ASI

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“…Over the years, many BIM literature reviews have been published. Whereas some focus on unique technical aspects of BIM (e.g., potential for rules to be checked to mitigate design errors) [37][38][39][40], others are more general [41,42], and some focus on green building and sustainable development [43,44]. Wu and Issa [45] point out the synergy between BIM and green building used to help achieve green objectives and boost sustainable development outcomes.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

Influence of Building Information Modeling (BIM) Implementation in High-Rise Buildings towards Sustainability

et al. 2021

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To secure full benefits without jeopardizing project feasibility, sustainability standards in high-rise building design should be included at all phases of the decision-making process. However, there are limited empirical studies on the influence of building information modeling (BIM) implementation in high-rise buildings. Implementing BIM is a viable technique to improve high-rise building sustainability performance. Therefore, the aim of this research is to explore the influence of BIM implementation in high-rise buildings by integrating the exploratory factor analysis (EFA) and structural equation modeling (SEM) approaches. Following a detailed review of the literature to identify critical success factors (CSFs) for BIM implementation, empirical evidence was gathered through a questionnaire survey with 205 stakeholders in construction projects. The EFA revealed five components, namely, productivity, visualization, coordination, sustainability, and safety improvement, all of which have a significant impact on the long-term construction of high-rise buildings. Moreover, SEM was conducted to develop the model for high-rise buildings. However, it has been revealed that awareness and usage level of BIM technology in high-rise buildings still appears to be limited. This scenario paves the way for future researchers to develop more models in the domain of high-rise buildings in order to improve sustainable development.

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“…The study of neural networks for the use of protection of transmission systems is not a new concept and has been ongoing for decades [34]. The use of neural network models finds application in several fields of research in engineering [35,36]. Kadam et al [37] opined that machine learning offers a broad range of algorithms that can be adapted to help with fault detection.…”

Section: Neural Networkmentioning

confidence: 99%

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

Okojie¹,

Idoko²,

Herbert³

et al. 2021

ASI

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Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use.

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Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review

Cited by 59 publications

References 135 publications

Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images

Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images

Influence of Building Information Modeling (BIM) Implementation in High-Rise Buildings towards Sustainability

Study of Transmission Line Boundary Protection Using a Multilayer Perceptron Neural Network with Back Propagation and Wavelet Transform

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