Purpose This study investigates how structured training impacts LLM adoption in architectural structural design. Despite LLMs’ potential, adoption barriers persist due to unclear implementation guidelines and insufficient training. Using UTAUT, this research examines how training influences performance expectancy, effort expectancy, social influence and facilitating conditions, while considering individual factors like gender and experience. Design/methodology/approach This study employs a longitudinal research design to examine how professional training influences the adoption of Large Language Models (LLMs) in architectural structural design. A total of 144 professionals participated in structured training, with pre- and post-training assessments measuring changes in technology acceptance using the unified theory of acceptance and use of technology (UTAUT) framework. Structural equation modeling (SEM) analyzed UTAUT constructs and behavioral intention, while multi-group analysis explored variations by gender and experience. Findings Results show significant improvements in technology acceptance post-training, especially in performance expectancy (ß: 0.509 → 0.810, p < 0.001). Training reduced gender disparities, with female professionals exhibiting greater confidence and behavioral intention. Junior professionals showed the highest gains. While training improved ease of use and social influence, its impact on facilitating conditions was minimal. Social implications This study demonstrates how structured training can reduce gender disparities in technology adoption within male-dominated professional fields. The finding that comprehensive LLM training significantly enhanced confidence and behavioral intention among female professionals provides actionable guidance for organizations seeking to promote diversity and inclusion in AI adoption. By accommodating diverse learning preferences and addressing experience-based differences, professional training programs can democratize access to emerging technologies and create more equitable opportunities for career advancement in technical disciplines. Originality/value This study extends UTAUT by showing how professional training dynamically influences technology adoption. Unlike prior research, it empirically demonstrates how structured training mitigates gender disparities and enhances confidence in AI use. The longitudinal design provides insights into evolving acceptance factors, offering guidance for organizations integrating LLMs in architecture and engineering.

Purpose This study investigates how structured training impacts LLM adoption in architectural structural design. Despite LLMs’ potential, adoption barriers persist due to unclear implementation guidelines and insufficient training. Using UTAUT, this research examines how training influences performance expectancy, effort expectancy, social influence and facilitating conditions, while considering individual factors like gender and experience. Design/methodology/approach This study employs a longitudinal research design to examine how professional training influences the adoption of Large Language Models (LLMs) in architectural structural design. A total of 144 professionals participated in structured training, with pre- and post-training assessments measuring changes in technology acceptance using the unified theory of acceptance and use of technology (UTAUT) framework. Structural equation modeling (SEM) analyzed UTAUT constructs and behavioral intention, while multi-group analysis explored variations by gender and experience. Findings Results show significant improvements in technology acceptance post-training, especially in performance expectancy (ß: 0.509 → 0.810, p < 0.001). Training reduced gender disparities, with female professionals exhibiting greater confidence and behavioral intention. Junior professionals showed the highest gains. While training improved ease of use and social influence, its impact on facilitating conditions was minimal. Social implications This study demonstrates how structured training can reduce gender disparities in technology adoption within male-dominated professional fields. The finding that comprehensive LLM training significantly enhanced confidence and behavioral intention among female professionals provides actionable guidance for organizations seeking to promote diversity and inclusion in AI adoption. By accommodating diverse learning preferences and addressing experience-based differences, professional training programs can democratize access to emerging technologies and create more equitable opportunities for career advancement in technical disciplines. Originality/value This study extends UTAUT by showing how professional training dynamically influences technology adoption. Unlike prior research, it empirically demonstrates how structured training mitigates gender disparities and enhances confidence in AI use. The longitudinal design provides insights into evolving acceptance factors, offering guidance for organizations integrating LLMs in architecture and engineering.

Lattice structures demonstrate a unique compressive behavior wherein the compressive strength increases and converges to a specific value as the number of unit-cell arrangements and overall dimensions of the lattice structure increase. In this study, the effects of the unit-cell size and cell arrangement on the compressive behavior of lattice structures were investigated to determine the convergence of compression characteristics at specific unit-cell configurations. Simple cubic lattice structures were designed and manufactured using powder bed fusion techniques. The lattice structures were characterized using universal testing machines. Experimental results revealed that the convergence of compressive strength was influenced by the size of the unit cell: the number of arrangements at which a specific compressive strength was reached increased as the unit-cell sizes decreased. Furthermore, specific convergence points were determined for different unit-cell sizes, providing valuable insights into the compressive strength behavior of lattice structures. This study emphasizes the significance of considering both unit-cell arrangements and overall dimensions when designing standard specimens for compression testing. Additionally, the methodology developed in this study can be applied to obtain an optimal configuration for desired strength characteristics.

This study proposed an optimization framework and methodologies to design edgeless lattice structures featuring fillet and multipipe functions. Conventional lattice structures typically experience stress concentration at the sharp edges of strut joints, resulting in reduced mechanical performance and premature failure. The proposed approach aimed to improve the compression behavior of lattice structures by introducing edgeless features. Through finite element analysis, the optimized fillet edgeless simple cubic unit cell with a fillet radius to strut radius ratio of 0.753 showed a 12.1% improvement in yield stress and a 144% reduction in stress concentration. To validate the finite element analysis, experimental compressive tests were conducted, confirming that the introduction of edgeless functions improved the compressive strength of lattice structures manufactured through additive manufacturing. The optimized fillet edgeless simple cubic lattice structure exhibited the most effective improvement. This approach has promising potential for lattice structure applications.

This study recently conducted various experiments using TiO2 to increase the utilization of economical TiO2 outer wall coating agents to solve fine dust, which is a major cause of air pollution. By comparing the performance of existing TiO2-containing application products, it shows how to utilize them for future use of photocatalysts. The experiment is largely divided into two stages. First, the effectiveness was verified by comparing the performance of reducing fine dust according to the mixing concentration of TiO2. Afterwards, experiments were conducted according to the number of applications, drying time, and surface materials using existing TiO2containing products.

In the era of the Fourth Industrial Revolution, artificial intelligence (AI) is a core technology, and AI-based applications are expanding in various fields. This research explored the influencing factors on end-user’s intentions and acceptance of AI-based technology in construction companies using the technology acceptance model (TAM) and technology–organisation–environment (TOE) framework. The analysis of end-users’ intentions for accepting AI-based technology was verified by applying the structure equation model. According to the research results, the technological factors along with external variables and an individual’s personality had a positive influence (+) on the perceived usefulness and the perceived ease of use of end-users of AI-based technology. Conversely, environmental factors such as suggestions from others appeared to be disruptive to users’ technology acceptance. In order to effectively utilise AI-based technology, organisational factors such as the support, culture, and participation of the company as a whole were indicated as important factors for AI-based technology implementation.