Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network’s input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach’s feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents.

In Vietnam, nano technology began to be interested in research and development, typically the seminar program of production research, silicon nano applications from waste materials and investment projects to build silicon nano manufacturing in Ho Chi Minh City. Researching the properties and applications of nano concrete in Vietnam with the desire to apply to some types of bridge and road structures today and step by step research on nano concrete application in designing some structure of bridges, roads, and other special works. With new materials used to determine the mechanical properties of materials and the characteristics of destruction at the limit of failure is essential. Moreover, high strength concrete materials are often brittle, so it is necessary to study measures to increase the plasticity to obtain the structure to ensure the requirements of the bearing and nano concrete with steel fibers. Steel fibers will make nano concrete increase tensile resistance in concrete.

The present study aims to investigate the effect of steel fiber addition on the behavior of the concrete beam in terms of crack formation, displacement, and deformation. The normal concrete (B20) was designed for making the beam with the target strength of 20 MPa. Different percentages of steel fiber (4% and 8% by volume) were added to the B20 mixture in order to evaluate the effectiveness of steel fiber addition to the concrete beams. The steel fiber concrete beams were subjected to the tests of crack formation, displacement, and deformation under loading. The numerical simulation by ANSYS computer software was also performed for comparison. As a result, the addition of steel fiber to the concrete beams was found to have positive effects as reducing both crack formation and the displacement as well as the deformation of the beams. Besides, the ANSYS modeling showed similar results as observed by the experiment.

Damage assessment is one of the most crucial issues for bridge engineers, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge. In this regard, the artificial intelligence (AI)-based approach seems a potential way to accomplish those obstacles. This study deploys a comprehensive campaign to determine all dynamic parameters of a pre-damage steel truss bridge structure. Based on the results of mode shape, natural frequency, and damping ratio, a finite element model (FEM) is created and keeps updating. The artificial intelligence network's input data will be analyzed and evaluation from damage cases. The trained artificial neural network model will be curated and evaluated to confirm the approach's feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system is showing good performance in terms of monitoring the structural behavior of the bridge under some unexpected accidents.

Withstanding extreme events is increasingly a significant challenge for the construction industry. Where civil infrastructures remain using traditional concrete, which has low tensile strength, poor durability, and weak crack resistance, in this regard, ultra-high-performance concrete (UHPC), with its outstanding mechanical properties and high strength, offers the prospect of wide application. This advanced technology allows for the fabrication of thin and light-dimensional structures to accelerate construction while increasing corrosion resistance to minimize maintenance intervention and extend the service life of the infrastructures. Despite this, UHPC is less eco-friendly due to consuming more cement than the usual material, which requires replacement materials, such as silica fume (SF) and rice husk ash (RHA), which are readily available from other local material production. This study proposes an experimental approach to assess the influence of SF and RHA content on the properties of UHPC. Different SF and RHA compositions will be adjusted to analyze their effects on slump flow, compressive strength, flexural strength, tensile strength, and the stress–strain relationship in UHPC tension testing. Based on the results, the most effective ratio is RHA replacing 50% of the SF in the UHPC mixture. Specialized tensile experiments reveal enhanced tensile strength with judicious RHA incorporation at 5-day and 28-day stages, particularly in initial crack and damage conditions. Stress–strain curves for 5% to 15% RHA samples show increased ductility, indicating that optimal RHA-SF ratios enhance UHPC cracking characteristics. Based on the results, a discussion on the appropriate proportions for utilizing most local materials will be derived, especially for regions of Vietnam. It is evaluated as a feasible and promising solution to reduce greenhouse gas emissions threatening global climate change.

Construction materials are at the forefront of global economic development as they provide the foundation for the infrastructure of other industries, with cementitious materials being predominantly used in construction projects. To promote sustainable development, alternative materials are added to cement mortar to increase durability and reduce emissions. In this regard, graphene oxide (GO) and fly ash (FA) are two alternative materials commonly used in cement mortar, which are readily available or are just the waste from other local material production. With different ratios, the amount of GO and FA can affect the properties of cement mortar positively or negatively. This study aims to determine the effects of GO and FA on cement mortar mixtures under material conditions. Research results show that 10 wt% FA and 0.036 wt% GO will give cement mortar the best physical and mechanical properties while ensuring other necessary properties, such as workability. When increasing FA to 30 wt% or GO to 0.05 wt%, the strength of the mortar mixture tends to decrease. Another issue is that the specific surface area of graphene is very high, which poses a significant challenge when uniform dispersion in the cement paste mixture is required. Polycarboxylate combined with a specific mixing sequence has demonstrated good dispersibility and high stability. Through this research, it is demonstrated that the addition of GO and FA has the potential for sustainable development of the construction industry by considering the contexts of the local recycled cementitious replacement materials.