Prediction of Hydration Heat of Mass Concrete Based on the SVR Model

Liu, Dunwen; Zhang, Wanmao; Tang, Yu; Jian, Yinghua

doi:10.1109/access.2021.3075212

Cited by 13 publications

(5 citation statements)

References 56 publications

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“…It can be argued that the classification approach is the most analytical approach implemented in the SHM system for bridges as can be seen in Table 5, which is expected because the SHM process is a classification problem from the machine learning point of view to compare between damaged and undamaged states of the structure. In the selected studies for this SLR, only 15.5% of the selected studies deploy regression for prediction operations [59,60,65,66,76,82,101]. For example, Xiao-Wei et al [59] propose a data-driven approach to predict the vibration amplitudes of girders and towers for early warning SHM.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…Only six studies deploy the regression approach where the SHM system is applied to predict the behavior of the bridge under different environmental conditions. For example, DUNWEN et al [66] deploy the regression approach to predict the hydration heat of mass concrete to apply a temperature control method in advance to reduce the possibility of thermal cracks.…”

Section: Utilization Of Analytical Approachesmentioning

confidence: 99%

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Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Sonbul

Rashid

2023

Sensors

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Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies (total = 45) in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements.

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Section: Discussion and Limitationsmentioning

confidence: 99%

Section: Utilization Of Analytical Approachesmentioning

confidence: 99%

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Sonbul

Rashid

2023

Sensors

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“…These case studies clarified the concrete mix ratio (including slag-containing cement) and the temperature variation caused by the ratio. Liu et al [18] employed a support vector machine model to predict the temperature field formed by the hydration exothermic action of mass concrete. They established correlations between the post-pouring temperature of pile foundations and various influencing factors, enabling short-term temperature predictions.…”

Section: Cmes 2024mentioning

confidence: 99%

Calculation of Mass Concrete Temperature and Creep Stress under the Influence of Local Air Heat Transfer

Zhang,

Su,

Chen

et al. 2024

CMES

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Temperature-induced cracking during the construction of mass concrete is a significant concern. Numerical simulations of concrete temperature have primarily assumed that the concrete is placed in an open environment. The problem of heat transfer between the air and concrete has been simplified to the concrete's heat dissipation boundary. However, in the case of tubular concrete structures, where air inlet and outlet are relatively limited, the internal air temperature does not dissipate promptly to the external environment as it rises. To accurately simulate the temperature and creep stress in tubular concrete structures with enclosed air spaces during construction, we establish an air-concrete coupled heat transfer model according to the principles of conjugate heat transfer, and the accuracy of the model is verified through experiments. Furthermore, we conduct a case study to analyze the impact of airflow within the ship lock corridor on concrete temperature and creep stress. The results demonstrate that enhancing airflow within the corridor can significantly reduce the maximum concrete temperature. Compared with cases in which airflow within the corridor is neglected, the maximum concrete temperature and maximum tensile stress can be reduced by 12.5°C and 0.7 MPa, respectively, under a wind speed of 4 m/s. The results of the traditional calculation method are relatively close to those obtained at a wind speed of 1 m/s. However, the temperature reduction process in the traditional method is faster, and the method yields greater tensile stress values for the corridor location. KEYWORDSConjugate heat transfer; temperature field; mass concrete; creep stress 1 IntroductionCurrently, the global annual volume of poured concrete exceeds 200,000 tons [1]. Various factors contribute to the susceptibility of mass concrete to cracking during construction, which directly reduces the bearing capacity, durability, and waterproofing of structures. Generally, the factors influencing concrete crack formation can be categorized into two groups: internal and external. Regarding internal factors, temperature stress is the primary cause of crack formation. The temperature difference

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“…Previous research has improved air conditioner operation efficiency through temperature prediction of data centers using regression analysis and support vector machines (SVM) [20]. Moreover, support vector regression (SVR) is applied to the hydration heat of mass concrete temperature prediction [29]. An artificial neural network (ANN) has been applied to predict the temperature change inside the tunnel [21].…”

Section: A Prediction Of Machine Temperaturementioning

confidence: 99%

A Data-Driven Recipe Simulation for Synthetic Rubber Production

Park

Bae

2022

IEEE Access

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To manufacture synthetic rubber, rubber manufacturers require optimal recipes to ensure that it satisfies the required quality standards. Several experiments are required to create the optimal recipe, which adversely affects not only the cost and time required but also the health of workers. Suppose the experimental results can be predicted in advance at the recipe design stage before direct experimentation. In that case, the cost of the experiment can be reduced, and the workers' health can be significantly less impacted. For this purpose, a method called the prediction walk model using a machine learning model was developed to generate the temperature trajectory in a kneading machine. A cross-updating method to predict the quality of the kneading operation is also proposed. From the results of the experiment, it was confirmed that the performance of the proposed models was superior to that of the existing prediction models.INDEX TERMS Synthetic rubber; rubber manufacturing; synthetic rubber recipe; prediction walk model

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Prediction of Hydration Heat of Mass Concrete Based on the SVR Model

Cited by 13 publications

References 56 publications

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Algorithms and Techniques for the Structural Health Monitoring of Bridges: Systematic Literature Review

Calculation of Mass Concrete Temperature and Creep Stress under the Influence of Local Air Heat Transfer

A Data-Driven Recipe Simulation for Synthetic Rubber Production

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