•A review of recent applications of emerging artificial intelligence (AI) methods is presented.•The methods of pattern recognition, machine learning, and deep learning are studied.•The advantages of ...employing novel AI methods in structural engineering are discussed.•Potential research avenues for using AI methods in structural engineering are identified.
Artificial intelligence (AI) is proving to be an efficient alternative approach to classical modeling techniques. AI refers to the branch of computer science that develops machines and software with human-like intelligence. Compared to traditional methods, AI offers advantages to deal with problems associated with uncertainties and is an effective aid to solve such complex problems. In addition, AI-based solutions are good alternatives to determine engineering design parameters when testing is not possible, thus resulting in significant savings in terms of human time and effort spent in experiments. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. The objective of this review paper is to summarize techniques concerning applications of the noted AI methods in structural engineering developed over the last decade. First, a general introduction to AI is presented and the importance of AI in structural engineering is described. Thereafter, a review of recent applications of ML, PR, and DL in the field is provided, and the capability of such methods to address the restrictions of conventional models are discussed. Further, the advantages of employing such algorithmic methods are discussed in detail. Finally, potential research avenues and emerging trends for employing ML, PR, and DL are presented, and their limitations are discussed.
The use of novel materials and new structural concepts nowadays is not restricted to highly technical areas like aerospace, aeronautical applications or the automotive industry, but affects all ...engineering fields including those such as civil engineering and architecture. This volume addresses issues involving advanced types of structures, particularly those based on new concepts or new materials and their system design, contributions highlight the latest developments in design, optimisation, manufacturing and experimentation.
Abstract The conventional design of steel structure objects relies on a first-order elastic analysis, where the entire object is treated as a set of individual structural elements requiring ...time-consuming, semi-empirical design calculations. Such an approach leads to inefficient design time and excessive material consumption and may additionally result in designing on the verge of structural safety. The AEC sector's technological and digitization advancement process forces designers to use advanced design methods. Hence, it is necessary to indicate the benefits of using effective optimization. The paper presents a comparative analysis of steel domes using two design approaches: traditional first-order analysis and an advanced second-order analysis. The latter method considers the influence of structural deformation on the magnitude of internal forces. Eight models were developed, varying in terms of the connection's stiffness. The work results identify the differences between the two selected design approaches and present opportunities for further structural performance of steel structures.
This paper provides a review of recent developments in research and design practice surrounding the structural use of stainless steel, with an emphasis on structural stability. The nonlinear ...stress-strain characteristics of stainless steel, which are discussed first, give rise to a structural response that differs somewhat from that of structural carbon steel. Depending on the type and proportions of the structural element or system, the nonlinear material response can lead to either a reduced or enhanced capacity relative to an equivalent component featuring an elastic, perfectly plastic material response. In general, in strength governed scenarios, such as the in-plane bending of stocky beams, the substantial strain hardening of stainless steel gives rise to capacity benefits, while in stability governed scenarios, the early onset of stiffness degradation results in reduced capacity. This behaviour is observed at all levels of structural response including at cross-sectional level, member level and frame level, as described in the paper. Current and emerging design approaches that capture this response are also reviewed and evaluated. Lastly, with a view to the future, the application of advanced analysis to the design of stainless steel structures and the use of 3D printing for the construction of stainless steel structures are explored.
•Constitutive modelling of stainless steel reviewed.•Influence of material nonlinearity of structural stability explained.•Current and emerging structural stainless steel design methods described.•Future trends, including design by advanced analysis and 3D printing, explored.
•Construction projects using 3D printing outlined and lessons learnt highlighted.•Review of metal 3D printing methods and their suitability for use in construction.•Discussion of existing metal 3D ...printing research and current work by the authors.•Current metal 3D printing use in other high-technology industries reviewed.•Opportunities and challenges for the wider adoption of 3D printing in construction presented.
3D printing, more formally known as additive manufacturing (AM), has the potential to revolutionise the construction industry, with foreseeable benefits including greater structural efficiency, reduction in material consumption and wastage, streamlining and expedition of the design-build process, enhanced customisation, greater architectural freedom and improved accuracy and safety on-site. Unlike traditional manufacturing methods for construction products, metal 3D printing offers ready opportunities to create non-prismatic sections, internal stiffening, openings, functionally graded elements, variable microstructures and mechanical properties through controlled heating and cooling and thermally-induced prestressing. Additive manufacturing offers many opportunities for the construction sector, but there will also be fresh challenges and demands, such as the need for more digitally savvy engineers, greater use of advanced computational analysis and a new way of thinking for the design and verification of structures, with greater emphasis on inspection and load testing. It is envisaged that AM will complement, rather than replace, conventional production processes, with clear potential for hybrid solutions and structural strengthening and repairs. These opportunities and challenges are explored in this paper as part of a wider review of different methods of metal 3D printing, research and early applications of additive manufacturing in the construction industry. Lessons learnt for metal 3D printing in construction from additive manufacturing using other materials and in other industries are also presented.
•Summarized collective experience of vision-based dynamic response measurement and SHM.•Reviewed basics and principles of vision-based sensor system.•Discussed the measurement error sources and ...mitigation methods.•Presented outlook of future directions of vision-based sensors for SHM.
To address the limitations of current sensor systems for field applications, the research community has been actively exploring new technologies that can advance the state-of-the-practice in structural health monitoring (SHM). Thanks to the rapid advances in computer vision, the camera-based noncontact vision sensor has emerged as a promising alternative to conventional contact sensors for structural dynamic response measurement and health monitoring. Significant advantages of the vision sensor include its low cost, ease of setup and operation, and flexibility to extract displacements of any points on the structure from a single video measurement. This review paper is intended to summarize the collective experience that the research community has gained from the recent development and validation of the vision-based sensors for structural dynamic response measurement and SHM. General principles of the vision sensor systems are firstly presented by reviewing different template matching techniques for tracking targets, coordinate conversion methods for determining calibration factors to convert image pixel displacements to physical displacements, measurements by tracking artificial targets vs. natural targets, measurements in real time vs. by post-processing, etc. Then the paper reviews laboratory and filed experimentations carried out to evaluate the performance of the vision sensors, followed by a discussion on measurement error sources and mitigation methods. Finally, applications of the measured displacement data for SHM are reviewed, including examples of structural modal property identification, structural model updating, damage detection, and cable force estimation.
Machine learning (ML) has become the most successful branch of artificial intelligence (AI). It provides a unique opportunity to make structural engineering more predictable due to its ability in ...handling complex nonlinear structural systems under extreme actions. Currently, there is a boom in implementing ML in structural engineering, especially over the last five years thanks to recent advances in ML techniques and computational capabilities as well as the availability of large datasets. This paper provides an ambitious and comprehensive review on the growing applications of ML algorithms for structural engineering. An overview of ML techniques for structural engineering is presented with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets. The review covers a wide range of structural engineering applications of ML including: (1) structural analysis and design, (2) structural health monitoring and damage detection, (3) fire resistance of structures; (4) resistance of structural members under various actions, and (5) mechanical properties and mix design of concrete. Both isolated members and whole systems made from steel, concrete and composite materials are explored. Findings from the reviewed literature, challenges and future commendations are highlighted and discussed. With available databases and ML codes provided, this review paper serves as a useful reference for structural engineering practitioners and researchers who are not familiar with ML but wish to enter this field of research.
Covalent organic frameworks (COFs) are a class of crystalline porous organic polymers with permanent porosity and highly ordered structures. Unlike other polymers, a significant feature of COFs is ...that they are structurally predesignable, synthetically controllable, and functionally manageable. In principle, the topological design diagram offers geometric guidance for the structural tiling of extended porous polygons, and the polycondensation reactions provide synthetic ways to construct the predesigned primary and high-order structures. Progress over the past decade in the chemistry of these two aspects undoubtedly established the base of the COF field. By virtue of the availability of organic units and the diversity of topologies and linkages, COFs have emerged as a new field of organic materials that offer a powerful molecular platform for complex structural design and tailor-made functional development. Here we target a comprehensive review of the COF field, provide a historic overview of the chemistry of the COF field, survey the advances in the topology design and synthetic reactions, illustrate the structural features and diversities, scrutinize the development and potential of various functions through elucidating structure–function correlations based on interactions with photons, electrons, holes, spins, ions, and molecules, discuss the key fundamental and challenging issues that need to be addressed, and predict the future directions from chemistry, physics, and materials perspectives.
AbstractPrecast concrete facilitates a construction method using durable and rapidly erectable prefabricated members to create cost-effective and high-quality structures. In this method, the ...connections between the precast members as well as between the members and the foundation require special attention to ensure good seismic performance. Extensive research conducted since the 1980s has led to new precast concrete structural systems, designs, details, and techniques that are particularly suited for use in regions of high seismic hazard. This paper reviews the state of the art of these advances, including code developments and practical applications, related to four different systems: (1) moment frames; (2) structural walls; (3) floor diaphragms; and (4) bridges. It is concluded from this review that the widespread use of precast concrete in seismic regions is feasible today and that the jointed connection innovation introduced through precast research leads to improved seismic performance of building and bridge structures.
