Research posters’ eBook: according to 1st WORKSHOP with “Focus on experimental testing of cement based materials”

COST Action TU 140

Numerical Study of Concrete

Concrete is one of the most widely used construction material in the word today. The research in concrete follows the environment impact, economy, population and advanced technology. This special issue presents the recent numerical study for research in concrete. The research topic includes the finite element analysis, digital concrete, reinforcement technique without rebars and 3D printing

New innovations in pavement materials and engineering: A review on pavement engineering research 2021

Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly supports the rapid development of the global social economy. New theories, new methods, new technologies and new materials related to pavement engineering are emerging. Deterioration of pavement infrastructure is a typical multi-physics problem. Because of actual coupled behaviors of traffic and environmental conditions, predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis. In order to summarize the current and determine the future research of pavement engineering, Journal of Traffic and Transportation Engineering (English Edition) has launched a review paper on the topic of “New innovations in pavement materials and engineering: A review on pavement engineering research 2021”. Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering, this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world. The content includes asphalt binder performance and modeling, mixture performance and modeling of pavement materials, multi-scale mechanics, green and sustainable pavement, and intelligent pavement. Overall, this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering

Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete

The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have been benchmarked against various soft-computing models and existing empirical equations. Sensitivity analysis has also been conducted to identify the most influential parameters to the SFRC shear strength

Advances in Asphalt Pavement Technologies and Practices

Unlike other construction materials, road materials have developed minimally over the past 100 years. However, since the 1970s, the focus has been on more sustainable road construction materials such as recycled asphalt pavements. Recycling asphalt involves removing old asphalt and mixing it with new (fresh) aggregates, binders, and/or rejuvenators. Similarly, there are various efforts to use alternative modifiers and technical solutions such as crumb rubber, plastics, or various types of fibres. For the past two decades, researchers have been developing novel materials and technologies, such as self-healing materials, in order to improve road design, construction, and maintenance efficiency and reduce the financial and environmental burden of road construction. This Special Issue on “Advances in Asphalt Pavement Technologies and Practices” curates advanced/novel work on asphalt pavement design, construction, and maintenance. The Special Issue comprises 19 papers describing unique works that address the current challenges that the asphalt industry and road owners face

Eleventh International Conference on the Bearing Capacity of Roads, Railways and Airfields

Innovations in Road, Railway and Airfield Bearing Capacity – Volume 1 comprises the first part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field

Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models

This reprint contains 15 papers published in the Special Issue entitled "Mechanical Behavior of Concrete Materials and Structures: Experimental Evidence and Analytical Models”. Dealing with the broad spectrum of the mechanical behavior of concrete materials and structures, the reprint includes experimental findings and numerical analyses using both conventional and advanced methodologies. This book presents contributions in the field of not only ordinary and prestressed concretes, but also special concretes, including high-strength, recycled, and fiber-reinforced concretes, for both structural and non-structural applications, and for the development of related numerical/analytical predictive models

浮遊選鉱法によって改質したフライアッシュを使用した再生骨材コンクリートの特性と適用性に関する研究

In general, this thesis starts with the development of new flotation equipment to bring fly ash up to industrial standards by reducing the carbon content. And by comparing the properties of the modified fly ash after flotation under different conditions, an optimum flotation time and additive content was found. After those the mechanical and physical properties of concrete with different fly ash and recycled aggregates content are measured by different experiments, compared with normal concrete and an attempt is made to find out the connection between some of these properties.北九州市立大

Deriving Generalized Temperature-Dependent Material Models for Masonry Through Fire Tests and Machine Learning

Masonry is one of the oldest and commonly used building materials in the construction industry. Among a variety of benefits, masonry provides low-cost construction, fire, and weather protection as well as thermal and sound insulation. In addition, masonry has superior material properties at elevated temperatures which is reflected by its slow degradation of its mechanical and thermal properties. Literature shows that we do not have a uniform material model that describes the mechanical degradation of masonry under fire conditions. As such, this limits the use of masonry in fire-based performance design of masonry structures. To bridge this knowledge gap, this thesis reviews regionally adopted fire testing methods on masonry and then presents findings from a fire experimental program aimed to explore the influence of elevated temperatures on the mechanical performance of concrete masonry units (CMUs). Our tests include heating and post heating evaluation of the compressive strength of CMUs exposed to realistic fire conditions. Then, this thesis delivers a methodology to derive generalized temperature-dependent material models for CMUs using statistical and Bayesian methods, as well as machine learning (by means of artificial neural networks). Finally, this work articulates limitations and research needs to be tackled in the near future