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

ReSCon '10, Research Student Conference: Book of Abstracts

The third SED Research Student Conference (ReSCon2010) was hosted over three days, 21-23 June 2010, in the Hamilton Centre at Brunel University. The conference consisted of oral and poster presentations, which showcased the high quality and diversity of the research being conducted within the School of Engineering and Design. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

RESCUE MANAGEMENT AND ASSESSMENT OF STRUCTURAL DAMAGE BY UAV IN POST-SEISMIC EMERGENCY

Abstract. The increasing frequency of emergencies urges the need for a detailed and thorough knowledge of the landscape. The first hours after a disaster are not only chaotic and problematic, but also decisive to successfully save lives and reduce damage to the building stock. One of the most important factors in any emergency response is to get an adequate awareness of the real situation, what is only possible after a thorough analysis of all the available information obtained through the Italian protocol Topography Applied to Rescue. To this purpose geomatic tools are perfectly suited to create, manage and dynamically enrich an organized archive of data to have a quick and functional access to information useful for several types of analysis, helping to develop solutions to manage the emergency and improving the success of rescue operations. Moreover, during an emergency like an earthquake, the conventional inspection to assess the damage status of buildings requires special tools and a lot of time. Therefore, given the large number of buildings requiring safety measures and rehabilitation, efficient use of limited resources such as time and equipment, as well as the safety of the involved personnel are important aspects. The applications shown in the paper are intended to underline how the above-mentioned objective, in particular the rehabilitation interventions of the built heritage, can be achieved through the use of data acquired from UAV platform integrated with geographic data stored in GIS platforms

A Systematic Literature Survey of Unmanned Aerial Vehicle Based Structural Health Monitoring

Unmanned Aerial Vehicles (UAVs) are being employed in a multitude of civil applications owing to their ease of use, low maintenance, affordability, high-mobility, and ability to hover. UAVs are being utilized for real-time monitoring of road traffic, providing wireless coverage, remote sensing, search and rescue operations, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. They are the next big revolution in technology and civil infrastructure, and it is expected to dominate more than $45 billion market value. The thesis surveys the UAV assisted Structural Health Monitoring or SHM literature over the last decade and categorize UAVs based on their aerodynamics, payload, design of build, and its applications. Further, the thesis presents the payload product line to facilitate the SHM tasks, details the different applications of UAVs exploited in the last decade to support civil structures, and discusses the critical challenges faced in UASHM applications across various domains. Finally, the thesis presents two artificial neural network-based structural damage detection models and conducts a detailed performance evaluation on multiple platforms like edge computing and cloud computing

A fairness-driven resource allocation scheme based on weighted interference graph in HetNets

—One of the most important 5G features is their support for heterogeneous networks (HetNets). Complementing the classic macrocell base stations (MBS), femtocell base stations (FBS) are beneficial in terms of extensive coverage, including indoor, and enhancement of capacity. Unfortunately, FBSs performance in 5G HetNets is affected by complex cross-tier and co-tier interferences, causing reduced quality of service (QoS) and unfairness among users. This paper proposes an innovative resource allocation (RA) algorithm for interference mitigation (IM) based on graph coloring techniques to improve QoS and interuser fairness. The proposed algorithm, named Weighted EdgeWeighted Vertex Interference Mitigation (WEWVIM), employs a weight to the directed edge corresponding to the interference strength from nearby base stations (BSs) and a weight to every vertex, indicating the color with the smallest interference or higher transmission rate. A region of interest (ROI) is formed to find the interfering BSs. Simulation results show that WEWVIM outperforms existing schemes in terms of fairness and QoS, including throughput, packet loss ratio (PLR), delay, and jitter. Index Terms—HetNets, Graph Coloring, Interference Mitigation, 5G, QoS, Resource Allocatio

Validation studies of the DOE-2 Building Energy Simulation Program. Final Report

This report documents many of the validation studies (Table 1) of the DOE-2 building energy analysis simulation program that have taken place since 1981. Results for several versions of the program are presented with the most recent study conducted in 1996 on version DOE-2.1E and the most distant study conducted in 1981 on version DOE-1.3. This work is part of an effort related to continued development of DOE-2, particularly in its use as a simulation engine for new specialized versions of the program such as the recently released RESFEN 3.1. RESFEN 3.1 is a program specifically dealing with analyzing the energy performance of windows in residential buildings. The intent in providing the results of these validation studies is to give potential users of the program a high degree of confidence in the calculated results. Validation studies in which calculated simulation data is compared to measured data have been conducted throughout the development of the DOE-2 program. Discrepancies discovered during the course of such work has resulted in improvements in the simulation algorithms. Table 2 provides a listing of additions and modifications that have been made to various versions of the program since version DOE-2.1A. One of the most significant recent changes in the program occurred with version DOE-2.1E. An improved algorithm for calculating the outside surface film coefficient was implemented. In addition, integration of the WINDOW 4 program was accomplished resulting in improved ability in analyzing window energy performance. Validation and verification of a program as sophisticated as DOE-2 must necessarily be limited because of the approximations inherent in the program. For example, the most accurate model of the heat transfer processes in a building would include a three-dimensional analysis. To justify such detailed algorithmic procedures would correspondingly require detailed information describing the building and/or HVAC system and energy plant parameters. Until building simulation programs can get this data directly from CAD programs, such detail would negate the usefulness of the program for the practicing engineers and architects who currently use the program. In addition, the validation studies discussed herein indicate that such detail is really unnecessary. The comparison of calculated and measured quantities have resulted in a satisfactory level of confidence that is sufficient for continued use of the DOE-2 program. However, additional validation is warranted, particularly at the component level, to further improve the program

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways