Sensing technologies are integral to the construction industry, enabling precise data collection, monitoring, and analysis for activities such as quality control, resource tracking, and progress monitoring. Despite their transformative potential, the adoption of these technologies is hindered by a persistent competency gap stemming from misalignment between academic curricula and industry expectations. This study addresses this gap by comparing the perspectives of academia and industry practitioners in the United States on the competencies (knowledge, skills, and abilities) required for implementing sensing technologies. Using a three-round Delphi study, this study engages academic representatives from accredited higher education institutions and industry practitioners from the US construction sector. Findings indicate a broad consensus between academia and industry on the importance of core competencies, including sensing technology operation, deployment relative to construction site conditions, and information modeling. Notably, areas of divergence include ethics and regulatory knowledge, research and development, and sensor signal processing techniques, reflecting differing priorities: academia emphasizes foundational research and ethical considerations, while industry focuses on hands-on, application-oriented competencies. This study contributes to the body of knowledge by identifying key areas of alignment and divergence, offering insights for curriculum development and workforce training. The results emphasize the need for integrated academic programs that address shared priorities while fostering academia–industry collaboration to bridge divergent priorities. By aligning academic training with industry needs, these findings can enhance workforce readiness, advance the adoption of sensing technologies, and drive innovation in the construction industry.

Insulating glass units (IGUs) contribute significantly to energy loss among building envelopes because they are responsible for approximately 30%–50% of the energy loss due to heat exchange. To reduce these losses, low-conductivity heavy inert gases such as argon, krypton, or xenon are introduced into the cavity of the IGU (i.e., IGU’s spacer). Argon is particularly favored due to its widespread availability and cost-effectiveness compared to others. However, the aging and deterioration of IGUs cause a notable decrease in Argon gas concentration, negatively affecting their insulating properties and subsequently increasing energy demand within buildings. Therefore, it is important to regularly evaluate the argon content to maintain the insulating effectiveness of IGUs. Destructive and nondestructive methods are currently available to assess the argon content inside the IGU’s spacer; however, they possess several drawbacks. Destructive methods are impractical for on-site IGU operations as they require spacer penetration, and nondestructive methods require costly equipment and have limited accessibility. Therefore, this study presents an experimental framework to assess the argon content within IGUs utilizing nondestructive evaluation (NDE) methods. The methodology is designed in four main steps: (1) designing an experimental setup for creating accurate gas mixtures of argon and air and directing the target gas mixtures to the IGU’s spacer; (2) conducting NDE tests on IGUs with various argon contents; (3) performing a validation test of the proposed methodology to verify the accuracy and repeatability of results obtained from Step 2; and (4) establishing a relationship between the properties of the utilized NDE method and argon content within IGU. The results show that ultrasonic testing (UT) is a reliable NDE method to evaluate argon content within IGUs, with ultrasonic energy serving as the most influential UT parameter for assessing argon content within the IGU’s spacer, as evidenced by a strong correlation with an R-squared value exceeding 0.9 across all the UT measurements. This research introduces a user-friendly solution that enhances the ease of assessing argon content within IGUs’ spacers, thereby facilitating the monitoring of their operational performance. The proposed methodology for evaluating argon content holds potential benefits for IGU manufacturers, researchers, building auditors, and inspectors in the construction industry domain. Moreover, it serves as a valuable tool for improving quality control across the manufacturing, maintenance, and operational stages of IGUs.

It is important to figure out the relationship between the microstructure of concrete and strength, which could, in turn, guide the mixing ratio design and optimize strength. To clarify the relationship between macrostrength and the microstructure of iron ore tailings (IOTs) concrete and establish the quantitative model, IOTs with five replacement ratios were used in this study to replace standard sand in concrete. The uniaxial compressive strength and splitting tensile strength of specimens for curing 28 days were tested, and the microstructure parameters were measured by mercury intrusion porosimetry and nuclear magnetic resonance. Microstructure parameters with a significant impact on strength were selected by single-factor analysis and the entropy value method from three microstructure characteristics: porosity, special aperture parameters, and pore gradation. Finally, the quantitative model of the strength microstructure was proposed, with the correlation coefficient R2 reaching 0.99. The proposed model was able to evaluate the strength of concrete from a microscopic point of view while providing a new way to predict the future strength. In addition, the pore gradation parameters had the greatest impact on compressive strength, while the special aperture parameters had the greatest influence on tensile strength. This finding can be used to guide the mixing ratio design and improve the construction process, which provides an optimized solution to achieve the target compressive and tensile strength requirements for specific practical applications.

Realizing low-carbon targets is greatly impacted by the building industry, which is a major source of energy consumption and greenhouse gas emissions in the context of global climate change. Building energy consumption can be effectively decreased, and energy efficiency increased with the use of outside wall insulation technology. However, existing studies on the thermal and mechanical properties of thin plaster technology for exterior wall insulation boards are limited and lack comprehensive comparative performance analysis. Therefore, this study adopts a combination of theoretical analysis and numerical simulation to analyze the performance of thin-plastering technology for an exterior wall insulation board using COMSOL (version 6.1) software. The experimental results indicate that the thin plaster exterior wall insulation system model is superior in terms of thermal insulation performance, with a 32.85% reduction in the heat transfer coefficient at thicknesses ranging from 100 to 150 mm. The heat transfer coefficient of the thin plaster exterior wall insulation system model using extruded polystyrene material is 15.94% lower than that of the sandwich insulation system model at a thickness of 250 mm. The stress and displacement fluctuations of the thin plaster exterior wall insulation system model are large under steady-state and transient conditions. Combining the experimental results, the study suggests increasing the thickness of the insulation layer, selecting highly efficient insulation materials, optimizing the material and layout of the tie members, strengthening the bond strength, controlling the humidity, and regular maintenance. In addition to offering a scientific foundation and useful advice for achieving the energy-saving and emission-reduction objectives of the construction industry, the study advances the innovation and development of building external wall insulation technology.

Despite its reputation as one of the least digitized sectors, the construction industry increasingly adopts artificial intelligence (AI) and construction technologies. However, previous research has not adequately explored the benefits of AI techniques and construction technologies nor the correlations between them. This study aims to fill this knowledge gap. To this end, the authors used a multistep research methodology. First, a systematic literature review of studies published between 1985 and 2022 was conducted to identify AI techniques and construction technologies. Second, content and frequency analysis were performed to determine the benefits associated with AI and technology adoption. Finally, a survey was developed, collected, and analyzed to (1) quantify the utilization of AI techniques and construction technologies; (2) assess the benefits of utilizing them; and (3) investigate the correlations between AI techniques, construction technologies, and key benefits. The study identified four AI techniques, 13 construction technologies, and 28 associated benefits. Results showed that widely adopted AI techniques are computer vision and knowledge-based systems (KBS) and the most adopted construction technologies include the global positioning system, building information modeling (BIM), the GIS, and unmanned aerial vehicles (UAVs). The key benefits are increased productivity, improved safety, improved monitoring and control, and informed decision-making. Correlations discovered in the study include (1) increased productivity, strongly associated with blockchain, BIM, and digital twin; (2) improved safety, closely linked with KBS, machine learning (ML), and robotics; (3) enhanced monitoring and control, associated with laser scanning, real-time systems, and UAVs; and, (4) informed decision-making, correlated with ML, natural language processing, and blockchain. This study provides a quantified comprehensive understanding of the benefits of AI and construction technologies, offering insights to prioritize innovations, and helping practitioners make informed investments for a competitive advantage.

The evolution of numerical models has become a reliable, precise, and fast alternative to represent real physical phenomena and solve engineering problems. This is of special importance in fire resistance tests, which are notoriously expensive and complex to conduct under laboratory conditions. Thus, the main objective of this study was to perform a numerical analysis of heat transfer in masonry walls made of clay blocks and cement mortar (rendering and joint) under fire conditions. The performance evaluation criterion was heat insulation, as stated by standards. Masonry wall systems were modeled with four different block geometries, each with dimensions of 14cm×19cm×29cm. From geometric wall configurations, additional models with rendering mortar on the faces exposed and unexposed to fire, as well as constant or temperature-dependent thermal properties of the material were constructed. A mortar joint of 1 cm was considered in between the blocks and a rendering mortar of 2 cm was assumed on the exposed and unexposed faces. All models were exposed to fire for 4 h with the application of the ISO standard fire curve. After exposure, the temperatures on the unexposed side were measured and compared with the thermal insulation criterion limit. The models were validated with the results of the reference studies. The results indicated that the reduced-dimension masonry wall (RMW) with the most hollow spaces and the lowest net to gross area ratio (RMWA) had the best thermal performance. The rendering mortar significantly increased the fire resistance times: in the case of RMWA, the rendered wall reached an average temperature of 123°C in 4 h while the unrendered wall reached 164°C in 2 h 42 min.

Augmented reality (AR) and mixed reality (MR) technologies are obtaining increasing research efforts in architecture, engineering, and construction (AEC) fields. Contrary to the well-documented research progress, there is a lack of understanding regarding the practical performance of AR/MR technology in real-world construction projects. This paper aims to understand the industry’s perceptions of the current implementation status, benefits, and challenges of adopting AR/MR technology. To explore the growing number of construction projects with AR/MR implementation experiences, the authors conducted an online survey and collected 158 valid responses from industry professionals. The results show that although the technology is sufficiently mature to bring practical benefits to construction use cases, the most significant benefit of AR/MR adoption remains in the business development category. The study also reveals that management challenges, such as workflow modifications and the need for skilled employees, are more significant than technical challenges. Based on the survey findings, the authors recommend promising research directions to facilitate the industry implementation of AR/MR technology. This paper contributes to the body of knowledge by identifying the gap between AR/MR research efforts and the early-stage industry implementation experience, as well as prioritizing research topics for realizing the AR/MR implementation vision of the AEC research community. The paper also highlights discrepancies between users and nonusers and recommends areas of industry collaboration to facilitate the widespread AR/MR industry adoption.

Within the built environment, different types of disciplines, such as architects, civil engineers, and construction (AEC), overlap in professional services. A similar overlap also happens at the university, with architects and civil engineers teaching in construction management-type programs. Academic unit affiliation (i.e., architecture, engineering, or construction management) marks a difference in the requirements for educator terminal degrees, teaching load, and research requirements. Further, there are differences in the discipline-specific coursework (lectures, laboratories, and/or studios) of the different disciplines. These differences may impact educators' perspectives about the online learning environment (OLE). Thus, this paper summarizes the findings of a research project focused on determining how program discipline affiliation differences affect educator perspectives for OLE. A survey methodology was utilized where an online Qualtrics survey was disseminated to AEC educators to determine their concerns about delivering online courses, lectures, design studios, and laboratory-type courses. Since the data collected were qualitative, the framework for analysis of qualitative data for construction and engineering disciplines was used. This research determined that department or program affiliation impacted perceptions of educators and identified a significant difference in perceptions between the two educator groups. This includes differences between educators' concerns and perceptions about which courses cannot be taught online.

The urban heat island (UHI) effect has detrimental impacts on building cooling demand, public and ecological health, and climate change. Because UHIs are caused by the concentration of construction materials that absorb and retain heat, buildings in urban areas present challenges and opportunities to mitigate them. Specifically, innovative building skin solutions, such as those covered with smart materials (SMs) that respond to environmental stimuli with their dynamic time and temperature-dependent behaviors, have significant potential to reduce the UHI effect. This research provides a review of the state-of-the-art applications of SMs in building skins for urban heat island mitigation (UHIM). It highlights the knowledge gaps and opportunities for future research with an extensive literature review and in-depth analysis. This research classifies the application of skin-integrated smart materials (SISMs) for UHIM into five main groups that included thermal, light, air pollution, humidity and ventilation control, and energy generation, and highlights their challenges and prospects.