The current study presents a comprehensive review of the benefits and barriers associated with the adoption of Building Information Modeling (BIM) in infrastructure projects, focusing on the period from 2013 to 2023. The research explores the manifold advantages offered by BIM, spanning the entire project life cycle, including planning, design, construction, maintenance, and sustainability. Notably, BIM enhances collaboration, facilitates real-time data-driven decision-making, and leads to substantial cost and time savings. In parallel, a systematic literature review was conducted to identify and categorize the barriers hindering BIM adoption within the infrastructure industry. Eleven studies were selected for in-depth analysis, revealing a total of 74 obstacles. Through synthetic analysis and thematic clustering, seven primary impediments to BIM adoption were identified, encompassing challenges related to education/training, resistance to change, business value clarity, perceived cost, lack of standards and guidelines, lack of mandates, and lack of initiatives. This review explores the benefits and barriers in the industry that are facing BIM adoption in infrastructure projects, giving an important perspective toward improving effective BIM adoption strategies, policies, and standards. Future directions for research and industry development are outlined, including efforts to enhance education and training, promote standardization, advocate for policy and mandates, and integrate BIM with emerging technologies.

In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLM) like OpenAI's GPT, Google's PaLM, and Meta's Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI's early-stage adoption within the construction sector. Given GenAI's unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors' opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture & engineering domains.

Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects. First, a mathematical model based on a partially observable Markov decision process is established to formulate the complex interactions of construction work, resource, and cash flows as well as uncertainty and variability of diverse influence factors. Meanwhile, to efficiently find the optimal solutions, a deep reinforcement learning (DRL) based method is introduced to realize the continuous adaptive optimal control of labor and material flows, thereby optimizing the work and cash flows. To assist the training process of DRL, a simulator based on discrete event simulation is also developed to mimic the dynamic features and external environments of a project. Experiments in simulated scenarios illustrate that our method outperforms the vanilla empirical method and genetic algorithm, possesses remarkable capability in diverse projects and external environments, and a hybrid agent of DRL and empirical method leads to the best result. This paper contributes to adaptive control and optimization of coupled work, resource, and cash flows, and may serve as a step stone for adopting DRL technology in construction project management.

Hollywood's striking Writers Guild of America (WGA) and SAG-AFTRA actors' union have filed a grievance with the United States's National Labor Relations Board (NLRB) against Comcast's NBCUniversal, accusing the company of blocking a picket area. The unions said on Tuesday that NBCUniversal infringed its freedom to picket and endangered its members by obstructing a public sidewalk next to the company's studio lot in California with an ongoing construction project. The WGA's complaint said NBCUniversal "forced picketers to patrol in busy streets with significant car traffic where two picketers have already been struck by a car". SAG-AFTRA said members had been forced "to picket at the unsafe crowded location, exacerbating the dire public safety situation to interfere with striking members' right to engage in the protected, concerted activity of picketing and patrolling outside the employer's premises during a lawful strike". Hollywood actors joined film and television writers on picket lines for the first time in 63 years last week as they demanded higher streaming-era pay and curbs on the use of artificial intelligence.

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.

The construction industry is undergoing a digital transformation, and one of the most significant changes is the adoption of artificial intelligence (AI) technologies. AI is being used to improve efficiency, safety, and quality in construction projects. In this blog post, we will explore how AI is changing the construction industry in 2023. AI is being used in various ways in construction projects. For example, drones are being used to survey construction sites and collect data.

AI can be unsettling for most people. Off the top of their head, many people think about robots taking over the human population or self-destructive devices. When coupled with construction, an industry that is notable for being slow to embrace technology; then you have reached a standstill as said industry is reluctant to change. On one corner is AI; a bit of an unknown element but one that is also dynamic and capable of many things. On the other, there is the construction sector; a dwindling industry frequently faced with challenges that threaten workers' safety, productivity, and resources.

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. Keywords: Natural Language Processing, ChatGPT, Scheduling, Generative Pre-training Transformer, Project Management, Construction 5.0, GPT 3.5 1 Introduction Natural Language Processing (NLP) combines areas such as linguistics, computer science, and Artificial Intelligence (AI) and focuses on the interaction between computers and humans using programs that are developed from large natural language data [1]. Selected applications of NLP in the construction industry include (1) Extracting information from construction documents: NLP techniques can extract relevant information, such as specifications, plans, and contracts, and convert it into a structured format that can be quickly processed by computers [2].

AI models can benefit as much from soft data such as personal anecdotes as much as hard data. It's well known among data science circles that the more diverse your set of training data, the more accurate your model will be. This includes structured, unstructured, and semistructured data. However, not all data is treated equally, especially when it comes to unstructured data. Soft data such as collective memory and personal anecdotes can be challenging to access, but they can help build better decision-making systems.

This study develops a framework for unmanned aerial systems (UASs) to monitor fall hazard prevention systems near unprotected edges and openings in high-rise building projects. A three-step machine-learning-based framework was developed and tested to detect guardrail posts from the images captured by UAS. First, a guardrail detector was trained to localize the candidate locations of posts supporting the guardrail. Since images were used in this process collected from an actual job site, several false detections were identified. Therefore, additional constraints were introduced in the following steps to filter out false detections. Second, the research team applied a horizontal line detector to the image to properly detect floors and remove the detections that were not close to the floors. Finally, since the guardrail posts are installed with approximately normal distribution between each post, the space between them was estimated and used to find the most likely distance between the two posts. The research team used various combinations of the developed approaches to monitor guardrail systems in the captured images from a high-rise building project. Comparing the precision and recall metrics indicated that the cascade classifier achieves better performance with floor detection and guardrail spacing estimation. The research outcomes illustrate that the proposed guardrail recognition system can improve the assessment of guardrails and facilitate the safety engineer's task of identifying fall hazards in high-rise building projects.