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The application of deep learning in construction has attracted increasing attention among researchers in recent years. In this review article, comprehensive scientometric analysis and critical review were performed to analyze the state- of-the-art literature on the application of deep learning in construction. This research used the science mapping method to quantitatively and systematically analyze 423 related bibliographic records retrieved from the Scopus database, and further, a critical review was performed on the collected themes of all the related publications. The results of the critical review indicate that deep convolution neural networks, you only look once, single-shot detectors, recurrent neural networks, residual neural networks, and fast region-based convolution neural networks have been the most widely used deep-learning methods in the construction industry. The most commonly addressed problems in the construction industry using deep-learning methods include classification of construction equipment, worker's safety helmet detection, ergonomics analysis, image enhancement, and feature extraction. This paper provides an in-depth understanding and big-picture overview of the existing literature along with the challenges and future direction of research on deep learning in construction. Key words: scientometric analysis, deep learning, critical review, construction industry, Introduction As the construction industry moves towards adopting advanced computer technologies to improve the efficiency of its operations, artificial intelligence is becoming increasingly popular among researchers who are developing innovative [...]

In northern Canada, due to the harsh weather and high labor cost, contractors prefer to using modular construction approach to complete heavy industrial projects, where mobile crane are used for onsite module installations. In current practice, module lifts are often planned manually by rigging engineers. With a large number of heavy lifts to be analyzed per project, the planning process is tedious and error prone. This paper represents a data-driven crane management system with three features: (1) identification of design errors in lifting planning; (2) responses to design changes; and (3) dynamic 3D trajectory-based lifting visualization. It covers two types of crane operations: pick from a fixed location, and pick and walking operation. This developed system helps reduce lifting time and improves safety and quality, where various lifting scenarios need to be analyzed. The system has been implemented at a collaborator company for demonstration and validation. Key words: heavy industrial project, crane management system, 4D visualization, 3D motion planning, lift path planning. Dans le nord du Canada, en raison des conditions meteorologiques difficiles et du cout eleve de la main- d'oeuvre, les entrepreneurs preferent utiliser une approche de construction modulaire pour realiser des projets industriels lourds, ou des grues mobiles sont utilisees pour les installations de modules sur place. Dans la pratique actuelle, les levage de module sont souvent planifies manuellement par les ingenieurs en montage. Avec un grand nombre de charges lourdes a analyser par projet, le processus de planification est fastidieux et sujet aux erreurs. Le present document presente un systeme de gestion de grue axe sur les donnees et comportant trois caracteristiques : (i) la determination des erreurs de conception dans la planification du levage; (ii) les reponses aux changements de conception; et (iii) la visualisation dynamique du levage fondee sur la trajectoire 3D. Ce systeme couvre deux types de grutage: la prise a un endroit fixe (PEF) et la prise et la marche (PM). Il permet de reduire le temps de levage et d'ameliorer la securite et la qualite, ou divers scenarios de levage doivent etre analyses. Le systeme a ete mis en oeuvre dans une entreprise collaboratrice aux fins de demonstration et de validation. [Traduit par la Redaction] Mots-cles : projet industriel lourd, systeme de gestion de grue, visualisation 4D, planification de mouvement 3D, planification de chemin de levage., Introduction In northern Alberta, the oil and gas reserve has led to mega heavy industrial construction projects that contributed to the growth of Canada's economy. The impact of the oil [...]

The research in value-oriented design reports the importance of displaying the interdependencies between product components and stakeholders' values, where it argues that the display of interdependencies is indispensable for the optimal decision-making and for the success of the value-oriented design. However, applications for value visualization in the construction industry lack one or both of the following: (1) the support for multi-value assessment and visualization and (2) the ability to visualize value(s) over time. These two shortcomings hinder the construction industry from fully embracing sustainable value-oriented design. Hence, this paper proposes a visualization framework to bridge the gap in the practice of value visualization regarding (i) the number of visualized values and (ii) time-based multiple-value visualization. The paper also contains a case study that utilizes a condominium building to show the implementation of the proposed framework and demonstrate how results can be interpreted and utilized in the design process. Key words: value visualization, value-oriented design, multiple-value visualization, construction industry, sustainability, building information modelling (BIM). La recherche en conception axee sur la valeur demontre l'importance d'afficher les interdependances entre les composants du produit et les valeurs des parties prenantes et on fait valoir que l'affichage des interdependances est indispensable pour la prise de decision optimale et pour le succes de la conception axee sur la valeur. Toutefois, les applications de visualisation de la valeur dans l'industrie de la construction sont depourvues de l'un ou l'autre des elements suivants: (1) le soutien pour l'evaluation et la visualisation de plusieurs valeurs et (2) la capacite de visualiser les valeurs au fil du temps. Ces deux lacunes empechent l'industrie de la construction d'adopter une conception axee sur la valeur durable. Par consequent, cet article propose un cadre de visualisation pour combler l'ecart dans la pratique de la visualisation de la valeur concernant (i) le nombre de valeurs visualisees et (ii) la visualisation de valeurs multiples basee sur le temps Le document contient egalement une etude de cas qui utilise un immeuble en copropriete pour montrer la mise en aeuvre du cadre propose et demontrer comment les resultats peuvent etre interpretes et utilises dans le processus de conception. [Traduit par la Redaction] Mots-cles: visualisation de la valeur, conception axee sur la valeur, visualisation de plusieurs valeurs, industrie de la construction, durabilite, modelisation des donnees du batiment (MDB)., Introduction The concept of value is a cornerstone that has facilitated successful societal development and civilizational advancement. This concept has been debated since the era of Greek philosophers who sought [...]

Highway bridges are vital components of infrastructure systems that support effective vehicle flow throughout the transportation network. Nevertheless, several factors, including the age of the bridge, operational characteristics, and exposure to the climate threaten the continuous function of such bridges. Because of this, it may be challenging to confidently anticipate the condition of bridges and prioritize needed maintenance tasks. In this article, we suggest a smart data-driven methodology for forecasting the condition of bridges based on a range of structural and operational aspects. In addition, several climatic factors were considered to assess the impact of environmental exposure on bridge conditions. Different machine learning algorithms including neutral network, support vectors machine, random forest, and others were trained utilizing historical bridge inspections in the U.S. Feature engineering and hyperparameter tuning techniques was used to identify the factors that have the most influence on the condition. With a mean relative error of 3.8%, GBT produced the most promising results. Additionally, the research demonstrated the predictive significance of some climatic parameters, particularly the freezing index. The developed model provides an accurate and timely assessment of their condition which can be leveraged to prioritize maintenance and renewal activities. [ABSTRACT FROM AUTHOR]

Target value design (TVD) principles set the main guidelines for the design- estimate process that allow the efficient exploration of available construction alternatives, thereby helping construction companies to reduce cost-to-design, cost-to-build, and improve the quality of construction projects. The successful application of TVD requires a clear understanding of the interactions among construction components. The proposed target cost modelling approach introduces an algorithmic factor-based framework to advance TVD that supports the design-estimate process by examining the relationships among building components, their direct and indirect impact on project overall cost and value. Construction factors control compatibility and performance analysis among available construction alternatives. Costing factors contribute to the development of mathematical costing models capable of automatically calculating the cost of compatible alternatives. Finally, rule-based analysis, developed under an appropriate programming environment, executes alternative value analysis to develop a detailed estimate with an improved overall value for construction projects. Key words: target costing, target value design, target cost modelling, value analysis, rule-based analysis, cost estimating, regression analysis. Les principes de conception selon une valeur cible (CVC) fixent les directives principales pour le processus de conception-estimation qui permettent d'explorer avec efficacite les options de construction disponibles, aidant ainsi les entreprises de construction a reduire le cout de conception, le cout de construction et a ameliorer la qualite des projets de construction. La mise en pratique reussie de la CVC requiert une comprehension claire des interactions entre les volets de construction. L'approche de modelisation du cout cible proposee presente un cadre algorithmique base sur un ensemble de facteurs afin d'avancerune CVC qui soutientle processus de conception-estimation en examinant les relations entre les elements de construction, leur effet direct et indirect sur le cout et la valeur du projet dans son ensemble. Les facteurs de construction determinent la compatibilite et l'analyse de rendement entre les options de construction disponibles. Des facteurs d'etablissement des couts contribuent au developpement de modeles mathematiques d'etablissement de couts en mesure de calculer automatiquement le cout des options compatibles. Finalement, l'analyse basee sur des regles, developpee en environnement de programmation approprie, effectue l'analyse de la valeur des options afin de developper une evaluation detaillee apportant une valeur globale amelioree pour les projets de construction. [Traduit par la Redaction] Mots-cles: gestion par cout cible, conception selon une valeur cible (CVC), modelisation de cout cible, analyse de la valeur, analyse basee sur des regles, estimation des couts, analyse de regression., Introduction and background Target costing (TC) has been utilized in various manufacturing industries in Japan during the planning and design stages to produce new products with higher quality and lower [...]

The growing prevalence of modular construction, while it offers benefits in terms of productivity and sustainability, has led to increased use of heavy mobile cranes and related resources on construction sites. One significant resource associated with crane use is the crane mat, which offers practical mobile crane ground support against poor soil-bearing capacity. Due to increased use of crane mats, crane mat layout plans/drawings have become increasingly significant in today's construction industry. The present work describes an automated crane mat optimization framework for preparing crane mat layout plans/drawings built on an agent-based greedy algorithm and reinforcement learning. The proposed framework employs these approaches to achieve the maximum area with the minimum number of crane mats. The proposed framework is found to decrease the time required for preparing a crane mat layout plan/drawing (approximately 97% time saving) with more uniform and efficient crane mat planning outcomes (approximately 63% crane mat material reduction). The research presented in this manuscript examined the largely unexplored topics of crane mat layout optimization and layout preparation, proposing an agent-based greedy algorithm and reinforcement learning (RL) approach for automated crane mat layout optimization as an innovative approach to developing sustainable crane mat layouts. This approach takes into account the site constraints and can be applied to mitigate crane mat crowding on construction sites. Crane mat optimization is applied using both methods (greedy and RL) to achieve the maximum area covered with the minimum number of crane mats used. The results demonstrate that the practitioner time spent preparing a crane mat layout plan/drawing can be reduced considerably, with more uniform and cost-effective crane mat optimization outcomes. Another outcome of this research is that developed approaches reduce the crane mat requirements compared with the outputs generated by the manual approach, thereby reducing the CO2 emissions associated with crane mat manufacturing and usage. [ABSTRACT FROM AUTHOR]