Thesis (M.S.)--Brigham Young University. Dept of Technology, 2006. / Includes bibliographical references (p. 47-49).

Construction progress monitoring allows schedule and/or cost deviations to be identified early enough to effectively implement corrective actions. At least 77% of transportation projects experience cost overruns, and as much as 75% of these overruns have been attributed to “real” construction management factors like progress monitoring. Progress is measured on road construction sites in terms of completion percentages at various activity and work package levels. This percentage is then used to identify schedule deviations and support the earned value analysis often used as the baseline for contractor progress payments. Unfortunately, the current methods for producing these completion percentages are not as correct or time efficient as they should be to enable effective project control. The objective of this research is to develop, test, and validate a novel solution for automatically producing completion percentages and progress status determinations that are more correct and time efficient than those generated in current practice. The proposed solution seeks to automatically detect incremental progress on road design layers in 3D as-built point cloud data generated using unmanned aerial photogrammetry and a novel data simulation approach. A parallel as-planned progress estimate is also automatically prepared using 4D information, and the progress status determinations are made by comparing the two results. This solution was tested on 15 datasets (13 simulated and 2 real-world) representing a variety of road designs and progress conditions. The method achieved an average 95% F1 score in layer detection on the real-world data, and mostly outperformed current practice in correctness. The automated processing of as-built and as-planned data to produce the progress estimate took 12 seconds for the real world data, which was indeed faster than the current practice equivalent. Although the research objectives were met, there remains room for further improvement, particularly in regards to the solution’s robustness to occlusions on the monitored surfaces.

Ineffective materials and equipment management continues to be a leading cause of poor performance on construction projects today. Many of the problems arise as a result of the inability to convey information pertaining to the location and status of construction material and equipment in an accurate and efficient manner. The integration of automated materials locating and tracking technologies (AMLTT) within the construction supply network presents a viable solution to this problem. The objective of this thesis is to further the understanding of the broader impact which these technologies could have on construction supply network management and the construction management process in general. This knowledge, however limited, is increasingly important as leaders in other industry sectors are beginning to report tangible benefits as a result of the integration of these technologies within their organizations’ supply networks. Using a modeling and simulation approach, the impact of AMLTT on three segments of the construction supply network, typical to most large scale and industrial construction projects, was investigated. The results indicate the potential for AMLTT to have a positive impact on resource allocation, productivity, risk mitigation, and improving the overall performance of the construction supply network in general.

Ineffective materials and equipment management continues to be a leading cause of poor performance on construction projects today. Many of the problems arise as a result of the inability to convey information pertaining to the location and status of construction material and equipment in an accurate and efficient manner. The integration of automated materials locating and tracking technologies (AMLTT) within the construction supply network presents a viable solution to this problem. The objective of this thesis is to further the understanding of the broader impact which these technologies could have on construction supply network management and the construction management process in general. This knowledge, however limited, is increasingly important as leaders in other industry sectors are beginning to report tangible benefits as a result of the integration of these technologies within their organizations’ supply networks. Using a modeling and simulation approach, the impact of AMLTT on three segments of the construction supply network, typical to most large scale and industrial construction projects, was investigated. The results indicate the potential for AMLTT to have a positive impact on resource allocation, productivity, risk mitigation, and improving the overall performance of the construction supply network in general.

Over the past decades, the construction industry lags further and further behind the manufacturing sector when productivity is considered. This is due to internal factors that take place on-site. Almost all of them are directly related to the way that productivity is monitored. Current practices for monitoring labour productivity are labour intensive, time - cost consuming and error prone. They are mainly reactive processes initiated after the detection of a negatively influencing factor. Although research studies have been performed towards leveraging these limitations, a gap still exists in monitoring labour productivity of multiple workers at the same time accurately, unobtrusively, cost and time efficiently. This thesis proposes a framework to address this gap. It hypothesizes that task productivity of construction workers can be monitored through their trajectory data. The proposed framework uses as input, video data streamed from cameras with overlapping field of view. It consists of two main methods. The output of the first is the input of the second. The first method tracks the location of workers across the range of a jobsite over time and returns their 4D trajectories. Such type of tracking requires that workers are matched under a unique ID not only across successive frames of a single camera (intra tracking) but also across multiple cameras (inter tracking). Existing tag-less studies fail to track construction workers due to the challenging nature of their working environments. Therefore, two novel computer vision-based algorithms are developed to perform both the intra and the inter camera tracking. The second method of the proposed framework converts the 4D trajectories of workers into productivity information. These trajectories are clustered into work cycles with an accuracy of 95%, recall of 76% and precision of 76%. Such work cycles depict the actual execution of tasks. The overall proposed framework features an average accuracy of 95% in terms of determining the total time workers spend on construction-related tasks.

This thesis presents a framework to conduct a quantity take-off (QTO) and cost estimate within the Building Information Modeling (BIM) Environment. The product of this framework is a model-based cost estimating tool. The framework addresses the cost uncertainty associated with the detailed information defining BIM model element properties. This cost uncertainty is due to the lack of available tools that address detailed QTO and cost estimation using solely a BIM platform. In addition, cost estimators have little experience in leveraging and managing information within semantic-rich BIM models. Unmanaged BIM element parameters are considered a source of uncertainty in a model-based cost estimate, therefore they should be managed and quantified as work items. A model-based system, which assists the estimators to conduct a QTO and cost estimate within the BIM environment, is developed. This system harnesses BIM element parameters to drive work items associated with the parameter’s host element. The system also captures the cost of scope not modeled in the design team’s BIM models. The system consists of four modules 1) establishing estimate requirements, 2) planning and structuring the estimate, 3) quantification and costing, and 4) model-based historical cost data collection. The complete system can produce a project cost estimate based on the 3D BIM Model. This framework is supported by a computation engine built within an existing virtual design and construction (VDC) model review software. The computation engine supports BIM authoring and reviewing BIM data. The Framework’s quantification and costing module was compared to existing methods in a case study. The outcomes of the model-based system demonstrated improved cost estimate accuracy in comparison to the BIM QTO method and improved speed compared to the traditional methods. The framework provides a systematic workflow for conducting a detailed cost estimate leveraging the parameters stored in the BIM models.

[ES] La industria de la construcción es un amplio sector industrial que abarca desde el diseño y la gestión de grandes infraestructuras como puentes hasta la construcción de viviendas civiles. Es mundialmente reconocido como un sector impulsor fundamental del Producto Interno Bruto, pero también se encuentra entre los de menor rendimiento y retraso en la adopción y explotación de mejoras tecnológicas. Estas limitaciones están induciendo a las partes interesadas a tomar prestadas e integrar muchas mejoras de otros campos industriales en el sector. Esta tendencia de digitalización se está extendiendo a lo largo de todo el ciclo de vida del proceso de construcción e identifica un enfoque desafiante debido al cambio de paradigma necesario de los sistemas físicos a los ciberfísicos. El concepto Industria 4.0 impulsó esta tendencia por lo que tanto en la academia como en la industria de la construcción se ha concretado como Construcción 4.0. Toma prestada de la Industria 4.0 la adopción de muchas tecnologías habilitadoras clave como Internet de las Cosas, Inteligencia Artificial y Fabricación Aditiva. Esta tesis investiga specíficamente esta integración tecnológica, centrándose en la aplicación de tales tecnologías habilitadoras en el campo de la construcción y considerando diferentes etapas en el ciclo de vida en diferentes tipologías de infraestructura. A partir de una investigación bibliográfica sobre sistemas inteligentes "holísticos" en la construcción de Edificios Inteligentes, a la manera de Gemelos Digitales, se estudia la influencia y la aplicación de tecnologías habilitadoras y herramientas TIC operativas relacionadas, como Internet de las Cosas y Big Data, desde una perspectiva de todo el ciclo de vida de las construcciones. Se estudia la fase de mantenimiento de grandes infraestructuras en materia de seguridad estructural y detección de fallos, mediante el desarrollo de un método de detección de daños en puentes ferroviarios de celosía metálica mediante inteligencia artificial. Luego se presenta una innovadora tecnología de fabricación aditiva para construcciones de gran altura. Consiste en una mejora de la tecnología de las grúas torre estándar con una extrusora personalizada, mientras que todo el sistema está controlado por un agente de inteligencia artificial. Concluimos que la Construcción 4.0 aún se encuentra en su etapa embrionaria. Se pueden obtener resultados más avanzados en la implantación tecnológica sobre infraestructuras existentes para su gestión de operación y mantenimiento debido al enfoque relacionado principalmente con la sensorización y análisis de datos. La innovación en la fase integrada de diseño/construcción sigue siendo más desafiante, debido a la necesidad de un paradigma completamente nuevo e innovaciones industriales en muchos campos diferentes. / [CA] La indústria de la construcció és un ampli sector industrial que abasta des del disseny i la gestió de grans infraestructures com a ponts fins a la construcció d'habitatges civils. És mundialment reconegut com un sector impulsor fonamental del Producte Intern Brut, però també es troba entre els de menor rendiment i retard en l'adopció i explotació de millores tecnològiques. Aquestes limitacions estan induint a les parts interessades a amprar i integrar moltes millores d'altres camps industrials en el sector. Aquesta tendència de digitalització s'està estenent al llarg de tot el cicle de vida del procés de construcció i identifica un enfocament desafiador a causa del canvi de paradigma necessari dels sistemes físics als ciberfísics. El concepte Indústria 4.0 va impulsar aquesta tendència pel que tant en l'acadèmia com en la indústria de la construcció s'ha concretat com a Construcció 4.0. Ampra de la Indústria 4.0 l'adopció de moltes tecnologies habilitants clau com a Internet de les Coses, Intel·ligència Artificial i Fabricació Additiva. Aquesta tesi investiga específicament aquesta integració tecnològica, centrant-se en l'aplicació de tals tecnologies habili- tants en el camp de la construcció i considerant diferents etapes en el cicle de vida en diferents tipologies d'infraestructura. A partir d'una investigació bibliogràfica sobre sistemes intel·ligents "holístics" en la construcció d'Edificis Intel·ligents, a la manera de Bessons Digitals, s'estudia la influència i l'aplicació de tecnologies habilitants i eines TIC operatives relacionades, com a Internet de les coses i Big Data, des d'una perspectiva de tot el cicle de vida de les construccions. S'estudia la fase de manteniment de grans infraestructures en matèria de seguretat estructural i detecció de fallades, mitjançant el desenvolupament d'un mètode de detecció de danys en ponts ferroviaris de gelosia metàl·lica mitjançant intel·ligència artificial. Després es presenta una innovadora tecnologia de fabricació additiva per a construccions de gran altura. Consisteix en una millora de la tecnologia de les grues torre estàndard amb una extrusora personalitzada, mentre que tot el sistema està controlat per un agent d'intel·ligència artificial. Concloem que la Construcció 4.0 encara es troba en la seua etapa embrionària. Es poden obtindre resultats més avançats en la implantació tecnològica sobre infraestructures existents per a la seua gestió d'operació i manteniment degut a l'enfocament relacionat principalment amb la sensorització i anàlisi de dades. La innovació en la fase integrada de disseny/construcció continua sent més desafiadora, a causa de la necessitat d'un paradigma completament nou i innovacions industrials en molts camps diferents. / [EN] The construction industry is a wide industrial sector ranging from the design and management of major infrastructures, such as bridges, to civil dwelling construction. It is worldwide acknowledged as a fundamental driving sector for the Gross Domestic Product, but it is also among the less performing and delayed ones in the adoption and exploitation of technological improvements. These limitations are inducing stakeholders to borrow and integrate many enhancements from other industrial fields into the sector. This digitalization trend is spreading through the entire life cycle of the construction process and identifying a challenging approach because of the paradigm shift needed from physical to cyber-physical systems. The Industry 4.0 concept boosted this trend so that both in the academy and in the construction industry it has been specified as Construction 4.0. It borrows from the Industry 4.0 the adoption of many key enabling technologies such as Internet of Things, Artificial Intelligence and Additive Manufacturing. This thesis investigates specifically this technological integration, focusing on the application of such enabling technologies in the construction field and considering different stages in the life cycle in varying infrastructure typologies. Starting from a literature investigation on "holistic" intelligent systems in Intelligent Buildings construction, in a Digital Twin fashion, the influence and the application of enabling technologies and related operative ICT tools such as Internet of Things and Big Data are studied, from a perspective of the whole constructions' life cycle. The maintenance phase of major infrastructures is studied concerning structural safety and fault detection, by developing a method to detect damages in railway steel truss bridges via artificial intelligence. An innovative additive manufacturing technology for high-rise constructions is then presented. It consists of an improvement with a custom extruder of standard tower crane technology, while the whole system is driven by an artificial intelligence agent. We conclude that Construction 4.0 is still at its embryonic stage. More advanced results are obtainable for the operation and maintenance management of existing infrastructures because of the already mature approach related to sensorization and data analysis. Innovation in the design/construction phase remains more challenging,because of the need for a completely new paradigm and industrial innovations in many different fields. / Parisi, F. (2023). Automation and Information Approaches to Support Maintenance and Production Management in the Construction Industry [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/192826