The chasm between funding and development is a gaping infrastructure void? As governments and corporations globally face mounting pressures to create a sustainable built environment, it has become clear that individuals cannot tackle this challenge on their own. To address the growing skills gap, numerous organizations are leveraging diverse forms of artificial intelligence (AI), including massive language models (LLMs) and machine learning (ML). While collectively they may not be equipped to address all current infrastructure challenges, they are still making progress by reducing costs, mitigating risks, and boosting efficiency.
Overcoming useful resource constraints
A severe shortage of highly skilled engineering and development professionals poses a significant challenge. By 2031, a projected 33% shortage of skilled professionals is anticipated within the United States, particularly in fields such as software, industrial, civil, and electrical engineering, resulting in unfulfilled job openings? In 2022, Germany revealed a shortage of approximately 320,000 STEM professionals, while another leading player in the field, Japan, predicted a shortfall of over 700,000 engineers by 2030. Given the propensity for engineering projects to extend over an extended period, such as repairing a damaged fuel pipeline, it’s reasonable to assume that the need for qualified engineers will consistently exceed supply unless and until a solution is implemented.
The complexities surrounding immigration and visa regulations for international engineering college students, coupled with the issue of retention in early-stage STEM careers, further exacerbate the challenges. While process duplication might pose a challenge for humans, AI’s capabilities enable seamless repetition.
According to Julien Moutte, Chief Technology Officer at Bentley Systems, there exists a staggering volume of manual effort required from engineers, which they describe as arduous and monotonous. Upwards of 25-40 percent of their workday is dedicated to condensing complex 3D designs into streamlined 2D formats, primarily using PDF converters. When tasks are automatable with AI-powered tools, employees can reclaim a significant portion of their work hours and redirect them to more valuable pursuits.
With the assistance of AI, identical drawings can be automated numerous times. By empowering engineers with the skills to pose the right queries and leverage AI effectively, we can alleviate the strain and pressure caused by repetitive tasks.
Notwithstanding this effort’s presence, several challenges arise. While customers of Large Language Models (LLMs), such as ChatGPT, are well-versed in the drawbacks of AI hallucinations, where the model can logically generate a series of phrases yet lack contextual comprehension of their meaning. While this may lead to unpredictable consequences, hallucinations in engineering can often prove significantly more perilous. “If AI-generated advice is provided, it must be thoroughly validated,” says Moutte. “Is that advice secure? The notion that a particular theory respects the legal guidelines of physics is unclear, as such guidelines do not exist. However, any theoretical framework or model in physics must adhere to the fundamental principles and laws of physics, which are well-established through empirical evidence and mathematical rigor. “It’s a waste of time for engineers to have to conduct a thorough assessment of each and every item.”
This may be offset by leveraging current firm instruments and merchandise to operate simulations, validating designs according to established engineering guidelines and design codes, thereby alleviating the need for engineers to perform validation tasks themselves.
Bettering useful resource effectivity
Approximately 500,000 tons of electronic waste, comparable to that generated by metal and concrete construction projects, are discarded annually on typical development websites in the USA and United Kingdom, with a significant proportion ending up in landfills, whereas nations such as Germany and The Netherlands have recently implemented recycling measures. As supply chain costs escalate, companies are being forced to reassess their operations for greater efficiency and long-term viability.
