Mechanical Behavior of Polyurethane Adhesives Applied to Timber Joints in Repair of Historical Timber Structures

Kwiecień, Arkadiusz; Rodacki, Konrad; Zając, Bogusław; Kozik, T.

doi:10.1007/978-3-319-99441-3_172

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Epoxy adhesives are already commonly used to bond steel bars, sheet metal or fiber-reinforced polymer composites in the most stressed areas of a building for reinforcement purposes [7][8][9]. Flexible adhesives, on the other hand, are used for repairing masonry and concrete structures and for seismic protection of buildings [10][11][12]. The durability of adhesive joints is affected by several factors, including materials, stresses, and environment.…”

Section: Introductionmentioning

confidence: 99%

Flexible Polyurethane Adhesives: Predictive Numerical Model Calibration through Experimental Testing at Elevated Temperature

La Scala,

Śliwa-Wieczorek,

Rizzo

et al. 2024

Applied Sciences

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The thermo-mechanical behavior of polyurethane adhesive joints in wood structures is a crucial aspect that needs to be understood to ensure the durability and safety of timber structures, especially in seismic regions. As mass timber, particularly cross-laminated timber, continues to gain popularity as a building material, it is important to pay special attention to the behavior of connections between the timber elements. The use of flexible polyurethane adhesives presents a promising alternative to conventional mechanical connections in seismic-resistant timber structures. This research highlights the potential of polyurethane-based joints at elevated service temperature, offers a promising alternative to traditional wood joints, and suggests viability for post-fire restoration of wood structures. The response at the interface between wood and polyurethane under flexural stresses is also evaluated, underscoring the broader application possibilities of flexible adhesives in wood construction for mechanical and physical improvements.

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Section: Introductionmentioning

confidence: 99%

Flexible Polyurethane Adhesives: Predictive Numerical Model Calibration through Experimental Testing at Elevated Temperature

La Scala,

Śliwa-Wieczorek,

Rizzo

et al. 2024

Applied Sciences

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“…Comply polymers are materials widely used in civil engineering technology. Exemplary applications comprise: connections in structural elements [1], reinforcement of traditional building materials like bricks or timber [2], reinforcement of historic or heritage buildings or structures [3], bonding layers in protection composites against earthquake-induced pounding of buildings [4]. The given examples do not, of course, exhaust the field of application of comply polymers in the building industry and civil engineering; however, they show some characteristic conditions in which these materials can work.…”

Section: Introductionmentioning

confidence: 99%

A new ANN rheological model of a comply polymer in temperature spectrum

2023

Archives of Civil Engineering

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The article presents modelling using artificial neural networks (ANN) of the phenomenon of creep of comply polymer SIKA PS which can be used in various applications in civil engineering. Data for modelling was gathered in compressive experiments conveyed under a set of fixed conditions of compressive stress and temperature. Part of the data was pre-processed by smoothing and rediscretisation and served as inputs and targets for network training and part of the data was left raw as control set for verification of prognosing capability. Assumed neural network architectures were one-and two-layer feedforward networks with Bayesian regularisation as a learning method. Altogether 55 networks with 8 to 12 neurons in varying structural configurations were trained. Fitting and prognosing verification was performed using mean absolute relative error as a measure; also, results were plotted and assessed visually. In result, the research allowed for formulation of a new rheological model for comply polymer SIKA PS in time, stress and temperature field domain with fitting quality of mean absolute relative error 1.3% and prognosis quality of mean absolute relative error 8.73%. The model was formulated with the use of a two-layer network with 5 + 5 neurons.

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