Recep Özkan, Fatemeh Mostofi, Fethi Kadıoğlu
et al.
The task of identifying high-value projects from vast investment portfolios presents a major challenge in the construction industry, particularly within the energy sector, where decision-making carries high financial and operational stakes. This complexity is driven by both the volume and heterogeneity of project documentation, as well as the multidimensional criteria used to assess project value. Despite this, research gaps remain: large language models (LLMs) as pretrained transformer encoder models are underutilized in construction project selection, especially in domains where investment precision is paramount. Existing methodologies have largely focused on multi-criteria decision-making (MCDM) frameworks, often neglecting the potential of LLMs to automate and enhance early-phase project evaluation. However, deploying LLMs for such tasks introduces high computational demands, particularly in privacy-sensitive, enterprise-level environments. This study investigates the application of the robustly optimized BERT model (RoBERTa) for identifying high-value energy infrastructure projects. Our dual objective is to (1) leverage RoBERTa’s pre-trained language architecture to extract key information from unstructured investment texts and (2) evaluate its effectiveness in enhancing project selection accuracy. We benchmark RoBERTa against several leading LLMs: BERT, DistilBERT (a distilled variant), ALBERT (a lightweight version), and XLNet (a generalized autoregressive model). All models achieved over 98% accuracy, validating their utility in this domain. RoBERTa outperformed its counterparts with an accuracy of 99.6%. DistilBERT was fastest (1025.17 s), while RoBERTa took 2060.29 s. XLNet was slowest at 4145.49 s. In conclusion, RoBERTa can be the preferred option when maximum accuracy is required, while DistilBERT can be a viable alternative under computational or resource constraints.
Dan Florin Nitoi, Oana Chivu, Florea Bogdan
et al.
This paper presents the activities carried out to improve the quality of certain composite structures by manufacturing them with the assistance of an ultrasonic field. As many composite materials use epoxy resins as base materials, an important problem was noted, namely their high curing time, as well as the problems of lack of adhesion and delamination, which are also known and experienced in the case of composite structures made with metallic materials as a support. The application of an ultrasonic field can successfully solve both problems. To demonstrate this improvement, the manufacturing of cylinders used in braking stands in the automotive industry was considered the main application. The proposed technology will be then extended to conveyor belts or to the manufacturing of other high-adhesion surfaces. This article presents the traditional method and the new ultrasonic field deposition technology. The design of the ultrasonic system is presented based on an analytical calculation, FEM modal analysis, followed by the construction of the ultrasonic system, as well as by bending tests and infrared thermography to demonstrate the advantages of presented method.
This paper comprehensively elaborates on the construction methodology, multi-dimensional evaluation system, and underlying design philosophy of CUFEInse v1.0. Adhering to the principles of "quantitative-oriented, expert-driven, and multi-validation," the benchmark establishes an evaluation framework covering 5 core dimensions, 54 sub-indicators, and 14,430 high-quality questions, encompassing insurance theoretical knowledge, industry understanding, safety and compliance, intelligent agent application, and logical rigor. Based on this benchmark, a comprehensive evaluation was conducted on 11 mainstream large language models. The evaluation results reveal that general-purpose models suffer from common bottlenecks such as weak actuarial capabilities and inadequate compliance adaptation. High-quality domain-specific training demonstrates significant advantages in insurance vertical scenarios but exhibits shortcomings in business adaptation and compliance. The evaluation also accurately identifies the common bottlenecks of current large models in professional scenarios such as insurance actuarial, underwriting and claim settlement reasoning, and compliant marketing copywriting. The establishment of CUFEInse not only fills the gap in professional evaluation benchmarks for the insurance field, providing academia and industry with a professional, systematic, and authoritative evaluation tool, but also its construction concept and methodology offer important references for the evaluation paradigm of large models in vertical fields, serving as an authoritative reference for academic model optimization and industrial model selection. Finally, the paper looks forward to the future iteration direction of the evaluation benchmark and the core development direction of "domain adaptation + reasoning enhancement" for insurance large models.
From NBODY1 to NBODY6 : The Growth of an Industry is the title of a 1999 invited review by Sverre Aarseth, for Publications of the Astronomical Society of the Pacific (PASP). I took this as an inspiration for the title of this paper; it describes how Sverres NBODY Industry has further grown since 90s of the previous century, and how it is further flourishing and hopefully developing, in his spirit, even after the sad news of his passing away reached us. My contact and friendship with Sverre started a few decades ago being sent to Cambridge to learn NBODY5, counting input parameters, and learning about the fact that even a sophisticated code (which had already at that time quite a history) requires permanent maintenance and bug fixes. Managed by Sverre, who relentlessly ran his code and responded to the widely spread crowd of customer colleagues. There has been a phase of massive and fast development and improvements due to vectorization, parallelization, GRAPE and GPU acceleration, and Sverre has been always on top of it if not ahead, but also fully adopting ideas of collaborators, once they tested well. NBODY6++GPU and NBODY7 entered the scene, and also recent new competitors, such as PETAR or BIFROST . We all have learnt a lot from Sverre, and strive to continue in his open-minded spirit, for open source and exchange. A striking evidence for the further growth of the industry is the number of papers here (and two of them follow in this session, but also in other sessions) using and further developing the aforementioned codes, as well as the occurrence of new and competing codes, which keep the field alive.
Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.
Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with cold-start and data-sparse scenarios. Recent advancements in pre-trained large language models (LLMs) with rich semantic knowledge, offer promising solutions to these challenges. However, deploying LLMs at scale is hindered by their significant computational demands and latency. In this paper, we propose a novel and scalable LLM-RecSys framework, LLMInit, designed to integrate pretrained LLM embeddings into CF models through selective initialization strategies. Specifically, we identify the embedding collapse issue observed when CF models scale and match the large embedding sizes in LLMs and avoid the problem by introducing efficient sampling methods, including, random, uniform, and variance-based selections. Comprehensive experiments conducted on multiple real-world datasets demonstrate that LLMInit significantly improves recommendation performance while maintaining low computational costs, offering a practical and scalable solution for industrial applications. To facilitate industry adoption and promote future research, we provide open-source access to our implementation at https://github.com/DavidZWZ/LLMInit.
Determining crop water requirements plays a crucial role in irrigation planning and, consequently, in the proper management of water resources in the agricultural sector. Therefore, this study examines the water consumption and requirements of agricultural crops in the Tajan River Basin, located in Mazandaran Province. The agricultural water needs and consumption within the study area include both net and gross crop water requirements. The net water requirement of field and orchard crops was estimated using data from the Plant Water Requirement Determination System of the Soil and Water Research Institute. Considering the irrigation efficiency for lands within the Tajan Plain, which fall under the Tajan irrigation network, as well as the upstream agricultural lands within the Tajan River Basin up to the plain's entrance (traditional irrigation system), the gross water requirement of each crop was estimated. The results of this study indicate that the net agricultural land area in the Tajan Plain is 68,100 hectares, with a projected water requirement of 433.5 million cubic meters. Additionally, the net agricultural land area in the Tajan River Basin up to the plain’s entrance is 15,300 hectares, with an estimated water requirement of 106 million cubic meters.
Construction industry, Engineering (General). Civil engineering (General)
This study investigates the empirical interaction between changing socioeconomic challenges and the construction sector in Cambodia. The empirical analysis applied uses data from the Statistical Yearbook of Cambodia (2021) to analyze the statistical relationship between socioeconomic , economic, demographic variables and construction. To do this, the study conducts a combination of regression analyses, Ordinary Least Squares in conjunction with Methods-of-Moments, while applying rigor to guarantee compliance with the CLR function assumptions. We find that the link between economic development and activity in construction is more complex than previously believed, with the role of the construction industry changing during economic development. Results show that the number of private dwelling construction projects can be explained by the ratio of primary education (-3.4), number of people employed in the educational sector (13.6), road construction (0.8), and net exports (-0.5). Future research should focus on i) potential endogeneity issues. This might emerge because activity might be conditional on the state of construction which, on the other hand, drives overall activity. ii) disentangling the effects of developments within domestic financial markets and their role in efficiently allocating financial capital to the real economy. Lastly, the significant impact of the Covid-19 crisis on the Cambodian economy, particularly the role of FP (fiscal policy) and MP (monetary policy) and efficient resource allocation within the construction industry. This aspect is crucial, as different industries responded variably to the shock and lock-down caused by Covid-19.
Sociology (General), Economic history and conditions
Wei Wei, Yogi Tri Prasetyo, Omar Paolo Benito
et al.
Building Information Modeling (BIM) has become integral to modern construction management in China, yet its successful implementation has been hindered by a shortage of skilled BIM personnel and high turnover rates. This study investigates the key factors influencing turnover intention among BIM workers in China through a structural equation modeling (SEM) approach. Survey data were collected from 558 BIM practitioners and researchers across various regions in China. The SEM results revealed that three work-related factors—management and staff cooperation, working hours, and satisfaction with salary and incentives—significantly affected BIM workers' turnover intentions. Notably, the influence of these factors on turnover intention was fully mediated by two pivotal attitudinal variables: job satisfaction and organizational commitment. Among the predictors, excessive working hours emerged as the most salient driver heightening turnover intention, whereas strong team cooperation and competitive, fair rewards reduce the propensity to leave by enhancing worker satisfaction and commitment. The integrated model developed in this research advances understanding of BIM workforce retention and can be generalized to similar project-based contexts. The findings provide an evidence-based foundation for policymakers and industry leaders to devise strategies (e.g., improving working conditions, strengthening organizational support) aimed at increasing job satisfaction and commitment, thereby mitigating turnover intention among BIM workers.
Ali Ebrahimi Kordlar, Hossein Safari, Mohammad Rozbeh
Objective: The construction industry has been increasingly criticized for its poor sustainability performance in recent decades, creating a chance for the sector to play a key role in global sustainability efforts. Rapid technological advancements and increasing construction project complexities have driven the need for flexible, sustainability-focused project management frameworks. This study introduces a fuzzy inference system designed to evaluate construction project sustainability, built on insights from extensive literature and expert input.Methods: To design the proposed model, the system inputs—criteria for evaluating the sustainability level of construction projects at various layers—were first identified. Next, the necessary if-then rules were developed based on expert opinions. The system output was determined in alignment with the research’s final objective. By offering a comprehensive assessment of construction project sustainability, the model enables organizations to identify their strengths and weaknesses, assess their current position, and make informed decisions to enhance their sustainable performance. Results: The output of the research includes a detailed analysis of the sustainability performance of construction projects. The designed model, along with its measurement tools, provides an opportunity for leaders and managers in the construction industry who are concerned about sustainability to gradually enhance their sustainability status and advance the sustainability level of projects. This model consists of three subsystems named the Direction, Execution, and Results subsystems. The aforementioned subsystems are the result of a literature review and are considered inputs to the final level of the model.Conclusion: The designed model serves as a tool to identify and implement improvement methods and potential areas for project advancement from a sustainability perspective. By utilizing this model, the quality of project execution in line with sustainability indicators, while addressing all three dimensions—economic, social, and environmental—improves continuously and proportionately.
The increasing frequency and severity of natural disasters—such as floods, storms, droughts, and earthquakes—have created a growing demand for temporary housing. These facilities must be rapidly deployed to provide safe, functional living environments for displaced individuals. This study proposes a design methodology for temporary housing exteriors using the text-to-image capabilities of generative artificial intelligence (GenAI) to address urgent post-disaster housing needs. The approach aims to improve both the efficiency and practicality of early-stage design processes. The study reviews global trends in temporary housing and the architectural applications of GenAI, identifying five key environmental factors that influence design: type of disaster, location and climate, duration of residence, materials and structure, and housing design. Based on these factors, hypothetical disaster scenarios were developed using ChatGPT, and corresponding exterior designs were generated using Stable Diffusion. The results show that diverse, scenario-specific design alternatives can be effectively produced using GenAI, demonstrating its potential as a valuable tool in architectural planning for disaster response. Expert evaluation of the generated designs confirmed their ability to adhere to text prompts but revealed a significant gap in terms of architectural plausibility and practical feasibility, highlighting the essential role of expert oversight. This study offers a foundation for expanding GenAI applications in emergency housing systems and supports the development of faster, more adaptable design solutions for communities affected by natural disasters.
Hot mix asphalt (HMA) has been widely used as a pavement material for decades because of its quick construction process and good engineering performance. However, its construction has to be performed at elevated temperature, causing significant energy consumption and hazardous emissions. Cold mix, which demands no heating in the construction process, is a cleaner and more environment-friendly paving technique. The cold mix binder, which bonds aggregates at ambient temperature, plays a key role in the environment-friendly cold mix pavement. However, in-depth understanding of the working mechanism and applications of cold mix binders is still lacking. To fill this gap, three different kinds of cold binders commonly used in pavement industry are extensively discussed, namely, the conventional bitumen emulsions, and the newly emerging epoxy resin and polyurethane.Bitumen emulsions are by far the most widely used cold binder in pavement construction for surface dressing, tack coat and cold mix. However, bitumen emulsions are inferior to HMA in terms of early strength and mechanical properties, which limited them from been used in structural layers. To improve the performance of bitumen emulsion, polymer latexes, such as SBR latex and waterborne epoxy resin, are commonly used as modifiers to prepare polymer modified bitumen emulsions. The incorporation of polymer latexes can significantly improve the performance of bitumen emulsion, including high- and low-temperature performance, adhesion with aggregate, and fatigue performance.Recently, polymer binders like epoxy resin and polyurethane have been introduced into the pavement industry. Epoxy resin and polyurethane are characterized as fast curing, remarkable mechanical strength, and strong adhesion with aggregate and substrates. However, there are still some shortcomings need to be addressed for the resin binders before they can be applied in large quantities, such as limited workability, insufficient resistance to weathering and high initial cost.This paper set out to provide a state-of-the-art review on the constitutions, properties, applications, and pros and cons of three cold binders, i.e., bitumen emulsion, epoxy resin and polyurethane, paving the way for future research and applications of these cleaner construction materials in pavement engineering.
Materials of engineering and construction. Mechanics of materials
This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of autonomous systems, including collaborative robots (cobots), into industrial processes is pivotal for efficiency and safety. The proposed solution employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), to identify transparent plastic bags under varying lighting and background conditions. Tracking algorithms and depth sensing technologies are utilized for 3D spatial awareness during pick and placement. The system addresses challenges in grasping and manipulation, considering optimal points, compliance control with vacuum gripping technology, and real-time automation for safe interaction in dynamic environments. The system's successful testing and validation in the lab with the FRANKA robot arm, showcases its potential for widespread industrial applications, while demonstrating effectiveness in automating the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader based on specific requirements and rigorous testing.
The high alkalinity and complex phase composition of red mud pose significant challenges for its direct utilization. The construction industry is widely regarded as the most viable option for large-scale consumption of red mud to mitigate its continuous accumulation globally. Activation treatment is a crucial prerequisite for preparing cementitious materials from red mud, but traditional methods either require high-temperature calcination of red mud or the use of substantial amounts of strong alkaline substances as alkaline activators, thereby increasing costs and hindering the large-scale application of red mud. This paper presents an alternative method where 4 % carbide slag is used as an activator to prepare cement-red mud-carbide slag-based composite pastes, substituting 10 %-90 % of masonry cement with red mud. As the red mud content increases, the compressive and flexural strengths of the hydrated pastes significantly decrease; the compressive strength of the material with 10 % red mud is 13.4 times that of the material with 90 % red mud. The addition of 4 % carbide slag enhances the compressive and flexural strengths of the red mud-based cementitious material, with increases of 1.67 %, 17.9 %, and 34.6 % in compressive strength at 3d, 7d, and 28d, respectively, and increases of 2.38 %, 6.67 %, and 7.36 % in flexural strength. This indicates that carbide slag is beneficial for activating the reactivity of red mud. There is an incremental correlation between the flexural and compressive strength of the cementitious material, and a fitting formula can be employed to quantify the relationship between red mud content and these strengths. Microstructural analysis confirms that the addition of carbide slag significantly optimizes the pore structure, promoting the formation of Ca(OH)2 and C-S-H gel, which leads to improved mechanical properties of the red mud-containing composites. This study provides a feasible solution for the extensive application of red mud within the cement industry.
Materials of engineering and construction. Mechanics of materials
Although the design-bid-build (DBB) system has poor stakeholder collaboration due to the separation between architects, engineers, and contractors in its linear process, resulting in longer schedules and cost overruns, DBB remains the most widely used project delivery system due to its benefits, such as higher competition and the owner’s absolute control over the project. Researchers and practitioners expect building information modeling (BIM) implementation to improve stakeholder collaboration due to its potential to bridge the collaboration gap between these stakeholders, ensuring better coordination and motivating cost reduction. However, the fragmented nature of DBB still poses significant challenges to effective stakeholder collaboration, impacting BIM implementation negatively. Therefore, the objective of this study is to identify and rank critical risk factors (CRFs) related to stakeholder collaboration impacting BIM implementation in the context of high-rise residential building projects delivered by DBB. By adopting a Delphi procedure, this study identified such CRFs by triangulating the knowledge from the literature and the experience of an expert panel recruited from the construction industry. Seven CRFs were selected and ranked through three rounds of a Delphi survey. Analysis revealed that the rankings remained unchanged despite the increasing degree of consensus among experts. The highest-ranking negative CRFs were “Inappropriate mindset of BIM collaboration process,” “Awareness of additional roles and responsibilities,” and “Disconnection in the information flow and BIM process.” A matrix of nine recommended solutions that can be applied across different CRFs was collected from the expert panel to improve the productivity of BIM collaboration under DBB. This study provides an in-depth understanding of the CRFs of BIM-based collaboration between stakeholders under a nonintegrated delivery system like DBB. The findings of this study can ensure successful project completion by creating awareness and allowing professionals and researchers to develop practical strategies for delivering BIM-based DBB projects.
Samuel A. Prieto, Eyob T. Mengiste, Borja García de Soto
Large language models such as ChatGPT have the potential to revolutionize the construction industry by automating repetitive and time-consuming tasks. This paper presents a study in which ChatGPT was used to generate a construction schedule for a simple construction project. The output from ChatGPT was evaluated by a pool of participants that provided feedback regarding their overall interaction experience and the quality of the output. The results show that ChatGPT can generate a coherent schedule that follows a logical approach to fulfill the requirements of the scope indicated. The participants had an overall positive interaction experience and indicated the great potential of such a tool to automate many preliminary and time-consuming tasks. However, the technology still has limitations, and further development is needed before it can be widely adopted in the industry. Overall, this study highlights the potential of using large language models in the construction industry and the need for further research.
Tatalina Oliveira, Ann Barcomb, Ronnie de Souza Santos
et al.
Context. Women remain significantly underrepresented in software engineering, leading to a lasting gender gap in the software industry. This disparity starts in education and extends into the industry, causing challenges such as hostile work environments and unequal opportunities. Addressing these issues is crucial for fostering an inclusive and diverse software engineering workforce. Aim. This study aims to enhance the literature on women in software engineering, exploring their journey from academia to industry and discussing perspectives, challenges, and support. We focus on Brazilian women to extend existing research, which has largely focused on North American and European contexts. Method. In this study, we conducted a cross-sectional survey, collecting both quantitative and qualitative data, focusing on women's experiences in software engineering to explore their journey from university to the software industry. Findings. Our findings highlight persistent challenges faced by women in software engineering, including gender bias, harassment, work-life imbalance, undervaluation, low sense of belonging, and impostor syndrome. These difficulties commonly emerge from university experiences and continue to affect women throughout their entire careers. Conclusion. In summary, our study identifies systemic challenges in women's software engineering journey, emphasizing the need for organizational commitment to address these issues. We provide actionable recommendations for practitioners.
In recent years, safety issues involving foundation pits have attracted extensive attention in the industry. The external angle of the foundation pit bears a greater load and is more prone to collapsing. Anchor support technology is one of the most widely used support technologies in construction engineering, but it has the problem of anchor rod crossing and colliding from the initial design stage, and this reduces anchoring force and eventually leads to many safety issues. Compared with traditional methods, Building Information Modeling (BIM) technology can save a lot of time as well as reduce costs, and applying it to concealed engineering can solve the problems that exist during the design stage and reduce unpredictable construction risks. This study proposes an optimization method based on BIM that can accurately and quickly optimize the drilling of anchor rods in the external angles of foundation pits. The results show that the proposed method can reduce the number of anchor rod collision points at the external angle of the foundation pit, minimize the loss of horizontal force caused by anchor rod collision, and ensure the safety and stability of the foundation pit support system.
This article focuses on fly ash aerated concrete, a new and very useful material in the construction industry and is basically a suspension of cement mortar with a maximum content of aluminum powder of 0.2 % by volume. A description of the conducted experimental study on the effect of the complete replacement of river sand with fly ash for various mixtures is given, and compressive strength indicators are also given.