Designing building structures presents various challenges, including inefficient design processes, limited data reuse, and the underutilization of previous design experience. Generative artificial intelligence (AI) has emerged as a powerful tool for learning and creatively using existing data to generate new design ideas. Learning from past experiences, this technique can analyze complex structural drawings, combine requirement texts, integrate mechanical and empirical knowledge, and create fresh designs. In this paper, a comprehensive review of recent research and applications of generative AI in building structural design is provided. The focus is on how data is represented, how intelligent generation algorithms are constructed, methods for evaluating designs, and the integration of generation and optimization. This review reveals the significant progress generative AI has made in building structural design, while also highlighting the key challenges and prospects. The goal is to provide a reference that can help guide the transition towards more intelligent design processes. 

Generative AI design for building structures

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of "advanced road materials, structures, equipment, and detection technologies". This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies. 

Review of advanced road materials, structures, equipment, and detection technologies

Abstract The frequent occurrence of building collapse accidents not only causes significant casualties, but also jeopardizes local economies. This paper adopts a combinatory assessment approach to showcase the lessons learned from a recent building collapse in Changsha, China. The proposed approach blends the system thinking approach and strategic environmental assessment (SEA) model. It delineates the causes of collapse and provide key leverage points for safety management. The results show that the primary causes for the collapse are the poor construction quality, illegal alterations, and lack of regulations enforcement. The management of rural housing construction in Hunan Province achieved a total score of 4 out of 30. It was also determined that the key prevention measures for abating these deleterious phenomena involve ensuring quality assurance/quality control, efficiently assessing safety risk, and timely performing structural health monitoring. This study is bound to enhance the understanding of collapse accidents and foster the achievement of sustainable cities and communities. 

An analysis approach for building collapse accident using system thinking approach and SEA model

Low-Altitude Intelligent Transportation: system architecture, infrastructure, and key technologies

Artificial Intelligence (AI) has grown and taken up challenging environmental issues. This technology helps accomplish sustainable development goals by helping us develop more effectively, use resources more sustainably and efficiently reduce waste and manage. Sustainable AI must be adapted to cater to the unique circumstances of each culture. The proper protections and procedures must be in place for AI systems, given the need for vast data to train the system. It has extensive applications for anything from ocean monitoring to climate change prediction modeling because of its skills in data processing. AI, Machine Learning (ML) and Robotic Process Automation (RPA) have helped these sectors survive with fewer people and varied limits, all while keeping prices low. Digital transformation is a culture, organization, and operations shift to increase customer value. This paper examines AI’s role in developing a sustainable environment. Primary goals and sustainable AI activities that contribute to sustainability are briefly covered. Finally, key applications of AI to sustainability were identified and explored. AI is transforming how we think, act, learn, and, of course, construct. It offers the potential to quickly identify inefficiencies and chances to boost productivity and sustainability across sectors. The building and construction sector needs to adopt AI into its routine procedures more than other sectors. With proper usage of AI, one may achieve increased output, simplify work, and eliminate inefficiencies so the staff can stay focused. AI can accomplish sustainable objectives when used appropriately and with sustainability at the center of its development. 

Significant applications of Artificial Intelligence towards attaining Sustainability

State-of-the-art AI-based computational analysis in civil engineering

Background: Despite marked improvements in pancreatic surgery, the high incidence and morbidity of pancreatic leak after resection has remained unchanged The current study investigated the safety and efficacy of bovine pericardium wrapping stump after distal pancreatectomy in a porcine model Methods: Thirty-two swine were randomly assigned to control and experiment groups to undergo conventional scalpel transection with single hand-sewn closure of the pancreatic remnant (control) or bovine pericardium wrapping stump combined with hand-sewn closure (experiment) Closed-suction drainage was collected and measured daily Animals were necropsied at 3 weeks postoperatively, and the pancreatic remnants were examined for histology Primary end points were the development of a pancreatic fistula defined as greater than threefold drain/serum amylase after the 3rd postoperative day, and the presence of undrained amylase-rich fluid collections/abscess Results: The incidence of pancreatic leak in the wrapping group was 63 versus 467% in the control group (p Conclusions: Bovine pericardium wrapping stump effectively reduced the incidence of pancreatic leakage after the distal pancreatectomy

/pdf/decreasing-pancreatic-leak-after-distal-pancreatectomy-598mumol0m.pdf

Decreasing pancreatic leak after distal pancreatectomy: bovine pericardium wrapping stump in a porcine model.

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State-of-the-art AI-based computational analysis in civil engineering

Decreasing pancreatic leak after distal pancreatectomy: bovine pericardium wrapping stump in a porcine model.