Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities

The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning

Machine learning methods in BIM-based applications : a review

This paper presents a survey of machine learning (ML) methods used in applications dedicated to the building and construction industry. A building information modeling (BIM) model, being a database system for civil engineering data, is presented. A representative selection of methods and applications is described. The aim of this paper is to facilitate the continuation of research efforts and to encourage bigger participation of database system researchers in the field of civil engineering

A Perspective on AI-Based Image Analysis and Utilization Technologies in Building Engineering: Recent Developments and New Directions

Artificial Intelligence (AI) is a trending topic in many research areas. In recent years, even building, civil, and structural engineering have also started to face with several new techniques and technologies belonging to this field, such as smart algorithms, big data analysis, deep learning practices, etc. This perspective paper collects the last developments on the use of AI in building engineering, highlighting what the authors consider the most stimulating scientific advancements of recent years, with a specific interest in the acquisition and processing of photographic surveys. Specifically, the authors want to focus both on the applications of artificial intelligence in the field of building engineering, as well as on the evolution of recently widespread technological equipment and tools, emphasizing their mutual integration. Therefore, seven macro-categories have been identified where these issues are addressed: photomodeling; thermal imaging; object recognition; inspections assisted by UAVs; FEM and BIM implementation; structural monitoring; and damage identification. For each category, the main new innovations and the leading research perspectives are highlighted. The article closes with a brief discussion of the primary results and a viewpoint for future lines of research

A review of the Digital Twin technology for fault detection in buildings

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Gaps and requirements for applying automatic architectural design to building renovation

The renovation of existing buildings provides an opportunity to change the layout to meet the needs of facilities and accomplish sustainability in the built environment at high utilisation rates and low cost. However, building renovation design is complex, and completing architectural design schemes manually needs more efficiency and overall robustness. With the use of computational optimisation, automatic architectural design (AAD) can efficiently assist in building renovation through decision-making based on performance evaluation. This paper comprehensively analyses AAD's current research status and provides a state-of-the-art overview of applying AAD technology to building renovation. Besides, gaps and requirements of using AAD for building renovation are explored from quantitative and qualitative aspects, providing ideas for future research. The research shows that there is still much work to be done to apply AAD to building renovation, including quickly obtaining input data, expanding optimisation topics, selecting design methods, and improving workflow and efficiency

Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

[EN] The Architecture, Engineering, and Construction (AEC) Industry is one of the most important productive sectors, hence also produce a high impact on the economic balances, societal stability, and global challenges in climate change. Regarding its adoption of technologies, applications and processes is also recognized by its status-quo, its slow innovation pace, and the conservative approaches. However, a new technological era - Industry 4.0 fueled by AI- is driving productive sectors in a highly pressurized global technological competition and sociopolitical landscape. In this paper, we develop an adaptive approach to mining text content in the literature research corpus related to the AEC and AI (AEC-AI) industries, in particular on its relation to technological processes and applications. We present a rst stage approach to an adaptive assessment of AI algorithms, to form an integrative AI platform in the AEC industry, the AEC-AI industry 4.0. At this stage, a macroscopic adaptive method is deployed to characterize ``Optimization,'' a key term in AEC-AI industry, using a mixed methodology incorporating machine learning and classical evaluation process. Our results show that effective use of metadata, constrained search queries, and domain knowledge allows getting a macroscopic assessment of the target concept. This allows the extraction of a high-level mapping and conceptual structure characterization of the literature corpus. The results are comparable, at this level, to classical methodologies for the literature review. In addition, our method is designed for an adaptive assessment to incorporate further stages.This work was supported by the CONICYT/FONDECYT/INICIACION under Grant 11180056 to Jose Garcia and the Spanish Ministry of Science and Innovation through the FEDER Funding under Project PID2020-117056RB-I00 to Victor Yepes.Maureira, C.; Pinto, H.; Yepes, V.; García, J. (2021). Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis. IEEE Access. 9:110842-110879. https://doi.org/10.1109/ACCESS.2021.3102215S110842110879

SAR Image Edge Detection: Review and Benchmark Experiments

Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art

Marshall Space Flight Center Research and Technology Report 2017

This report features over 60 technology development and scientific research efforts that collectively aim to enable new capabilities in spaceflight, expand the reach of human exploration, and reveal new knowledge about the universe in which we live. These efforts include a wide array of strategic developments: launch propulsion technologies that facilitate more reliable, routine, and cost effective access to space; in-space propulsion developments that provide new solutions to space transportation requirements; autonomous systems designed to increase our utilization of robotics to accomplish critical missions; life support technologies that target our ability to implement closed-loop environmental resource utilization; science instruments that enable terrestrial, solar, planetary and deep space observations and discovery; and manufacturing technologies that will change the way we fabricate everything from rocket engines to in situ generated fuel and consumables

DiSECCS - final summary report. Work packages, 1 - 4

Seismic techniques comprise the key geophysical toolset for imaging and characterising induced changes in the subsurface associated with human activity. This ability to observe and quantify changes in fluid saturation, pressure and geological stress and strain using active and passive seismic techniques has critical application to the monitoring of geological CO2 storage. The DiSECCS project (Diagnostic Seismic Toolbox for Efficient Control of CO2 Storage) has developed seismic monitoring tools and methodologies to identify and characterise injectioninduced changes, whether of fluid saturation or pressure, in storage reservoirs. We have developed guidelines for the monitoring systems and protocols required to maintain the integrity of storage reservoirs suitable for large-scale CO2 storage. The focus is on storage in saline aquifers (comprising the largest potential global storage resource), where considerable amounts of in situ water have to be displaced and both pressure and two-phase flow effects have consequences for storage integrity and storage capacity. Underground storage of CO2 is associated with significant levels of public concern. A better understanding of this is a key element of establishing monitoring protocols to instil wider public confidence in CO2 storage. DiSECCS draws on analogue activities, such as ‘fracking’ for shale gas, in conjunction with a discursive process involving lay participants, to gain insights into how people engage with similar underground activities and how controversies surrounding particular projects develop and evolve