A new method of making particleboard with a formaldehyde-free soy-based adhesive

Particleboard is widely used for making bookshelf, furniture, cabinets and many other interior products. At present, particleboard is mainly produced with wood particles and urea-formaldehyde (UF) resins. The emission of carcinogenic formaldehyde in the production and use of particleboard has generated an urgent need for development of a formaldehyde-free wood adhesive for making particleboard. Formaldehyde is derived from natural gas whose reserve is finite. It is also desirable to develop a wood adhesive from renewable material for making particleboard. Soy flour is inexpensive, abundant, renewable and readily available. A formaldehyde-free wood adhesive consisting of soy flour (SF) and a curing agent (CA) CA1000 has been used for commercial production of interior plywood since 2004. However, this CA-soy adhesive has high viscosity and is difficult to be sprayed onto wood particles with a conventional spraying nozzle. This study developed a new method of using this CA-soy adhesive for making particleboard. This new method involved the coating of wood particles with a dilute soy slurry in water, the drying of the soy-coated wood particles, the spraying of the CA onto the dried soy-coated wood particles, the formation of a particleboard mat with the CA-soy-coated wood particles and the hotpressing of the mat into particleboard. The high viscosity of the adhesive was no longer an issue with this new method. For investigation of the effectiveness of this new method, effects of particleboard density, adhesive usages for both core and face particles, the solids content of the soy slurry, hot-press time, hot-press temperature, the storage time of the soy-coated wood particles, and the soy/CA ratio on the internal bond strength (IB), the modulus of rupture (MOR), and the modulus of elasticity (MOE) of the resulting particleboard were investigated in detail. Results demonstrated that this new method had wide operational windows for making particleboard and allowed the strengths of particleboard bonded with this CA-soy adhesive to exceed the industry requirements of M-2 particleboard. The optimal conditions of using this method for making particleboard in terms of enhancing the IB, MOR, and MOE were: 760 kg/m³ of the particleboard density, 11 wt% resin usage for the core particles, 12 wt% resin usage for the face particles, 20 wt% solids content of the soy slurry, 180 °C of the hot-press temperature, 224 s of the hot-press time, 1:7 CA/SF weight ratio, and 36 h of the storage time for the wet soy-coated wood particles

Exploring ASEAN Fly Ash for Enhancing Cement Hydration and Service Life Prediction of Portland Cement Mortar

The durability of cementitious materials can be improved with the widespread utilization of fly ash (FA). Although FA has been available for use in cement and concrete industries for decades, there is still a practical barrier associated with its application. The difficulty stems from its wide variety and heterogeneity. The purpose of this research is to conduct both experimental and numerical investigations to achieve a better understanding of managing the variation of FA, which reflects its durability. The chemical properties and particle size distribution of FA from five distinct sources in ASEAN region were analyzed. In addition, the degree of reactivity, flow, toughened porosity, and apparent chloride diffusivity coefficients of blended FA-cement systems were studied (Da). The Life365 service life model was executed. Using analysis of variance (ANOVA) and sensitivity analysis using linear regression, the experimental outcomes were statistically examined. Having a 15% FA replacement level resulted in a roughly 70% decrease of the Da value, extending its serviceability by around 13%. The chemo-physical processes in multi-scale structures were shown to be the most important element by statistical analysis, and the degree of response in blended FA-cement systems and its toughened porosity were found to be among the most beneficial aspects affecting its durability

Investigation of Fineness and Calcium-Oxide Content in Fly Ash from ASEAN Region on Properties and Durability of Cement–Fly Ash System

Fly ash is a ubiquitously used pozzolan in cementitious material. Its technical knowledge has been widely studied for several decades and seems very well-established. However, a practical challenge currently exists. Different fly ash, even in different batches produced from similar power plant, has a wide variation of properties. To better control the wide variation of properties, this study aims to assess the effects of cementitious mixtures containing different fly ash properties (degree of fineness and CaO content) on heat release, flow, compressive strength, chloride ion penetration resistance, and carbonation resistance. Results from statistical analysis of 270 tested data of fly ash in various ASEAN countries indicate that the fineness of fly ash particle influences on the compressive strength, chloride ion penetration resistance, and carbonation resistance. Whereas, the CaO content of fly ash only statistically impacts on the compressive strength, but not on the chloride ion penetration resistance and carbonation resistance. To reduce the current practical challenge of the wide variation of cement–fly ash composite properties, their durability properties can be improved by adding smaller fly ash particle in cement system. The control of fly ash production by delivering its optimal degree of fineness is more important than selecting the optimal CaO content of fly ash. The study offers a deep technical value differentiating between effects of its fineness and CaO content such that the fly ash producers and consumers can realize and offer the optimized fly ash for good product quality

Microplastics in construction and built environment

Plastics have been extensively used in the building and construction industries for decades. However, the more plastics are utilised, the more microplastics are released. This review and analysis article summarises and organises the knowledge from 211 current related publications published in 2014–2022. The review and analysis explain the kinds of plastics employed in construction and built environment. Fabrics or textiles, fibres and plastics in cementitious systems, paints, tyres and roads are discussed. The entry points of microplastics into the human body are reviewed next, followed by the management of recycled wastes. The important research gaps and possible solutions include using high-strength concretes and surface-hardening agents is suggested to encapsulate the microplastics inside the matrix; DPSIR model analysis can be holistically adopted for each composite; innovative bio-chemical technology like self-healing concrete and bio-degradable plastics can be a viable choice; and social science, law and urban planning can support awareness and comprehension

A novel framework for effective structural vulnerability assessment of tubular structures using machine learning algorithms (GA and ANN) for hybrid simulations

Seismic vulnerability assessments are conventionally conducted by using sophisticated nonlinear analytical models, leading to aggressive computational demands. Previous attempts were made to reduce computational efforts for establishing vulnerability assessment of structures; however, the area of super tall and tubular structures still faces considerable lack. Advent of efficient machine learning (ML) has enabled engineering practitioners to automate the processes for fragility analysis; however, its application for high-rise tubular structures is not yet exploited, and most implementations are limited to basic ML. In this work, an attempt was made to reduce computational demand for the fragility assessment process for tubular structures by employing genetic algorithms (GAs) for nonlinear structural modeling, and development of artificial neural network (ANN) using deep learning for fragility development. Consequently, a simple lumped parameter model had been developed using open-source code of ZEUS-NL, containing parameters selected by GA to acutely account for convoluted interactive behavior of structural systems and dynamic demands. Subsequently, incremental dynamic analysis (IDA) was performed on the optimized model. A new framework has been established to develop and train ANN architecture by amalgamating Weka’s capability of data preprocessing with deep learning. The established ANN model resulted in correlation coefficient of 0.9972 and R 2 of 0.95, demonstrating adequate performance