Borja García de Soto Lastra


Last Name

García de Soto Lastra

First Name

Borja

ORCID

0000-0002-9613-8105

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Publications 1 - 10 of 11
  • Improving the planning and design phases of construction projects by using a Case-Based Digital Building System
    Item type: Journal Article
    García de Soto Lastra, Borja; Streule, Thomas; Klippel, Michael; et al. (2020)
    International Journal of Construction Management
    The construction industry is known for having well-defined and systematic processes for the different phases of a project. Improvements, however, are possible with respect to how information is used in these phases and how the experience gained from previous projects is exploited. In this paper, an example of how new technologies can be used to improve the planning and design of buildings is shown. In the example, a Case-Based Digital Building System (CB-DBS) in which every building as an integrated structural system consisting of highly parameterized standard elements is used in the planning phases of projects. In the planning phases, case-based reasoning is used to identify similar completed projects that would enable faster starts of new projects. In the design phases, case-based design is used to identify similar portions of completed projects that would enable faster designs of the more detailed parts of the buildings. A proof of concept using housing projects from a design–build–operate company based in Switzerland is used to test the viability of the proposed system. It is shown that the CB-DBS enabled faster planning and design phases of new projects and improved exploitation of the knowledge gained in previous projects.
  • Predicting road traffic accidents using artificial neural network models
    Item type: Journal Article
    García de Soto Lastra, Borja; Bumbacher, Andreas; Deublein, Markus; et al. (2018)
    Infrastructure Asset Management
  • Transshipment approach to coordinate materials for a contractor’s project portfolio
    Item type: Journal Article
    Chen, Qian; García de Soto Lastra, Borja; Adey, Bryan T. (2022)
    International Journal of Construction Management
    The challenges to coordinate material supply and dynamically changing demand always lead to construction interruptions or a considerable waste of materials on-site. Mainstream research has provided various advanced digital solutions to solve these problems; however, they have not addressed how to make reliable decisions with digital models to manage the demand fluctuations of construction materials. This study proposes a transshipment approach to enable the lateral sharing of perishable materials and optimize material allocation for a contractor’s project portfolio. The transshipment approach includes two main steps. First, the daily material supply and demand data are collected from a continuously updated schedule and three-dimensional models as input for calculating unused material quantities. Second, an evolutionary optimization algorithm is used for optimizing the transshipment quantities with minimal cost. As proof of concept, the proposed transshipment approach is demonstrated by looking at a portfolio of seven building projects managed by the same contractor. The demonstration shows that the allocation of the unused materials helps to avoid waste and reduce costs from over-ordered materials by around 52%. As a result, this also leads to improved coordination between contractors and suppliers and better material flow in construction projects.
  • Estimating bridge characteristics with only situation characteristics using Bayesian networks
    Item type: Conference Paper
    Panopoulos, Vassilis; Bougas, L.; García de Soto Lastra, Borja; et al. (2021)
    Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations
    When deciding where a new road or rail line should be built, it is useful to have some idea as to the characteristics of the bridges to be built. One way to do this is to have engineers develop preliminary designs for the bridges that would likely be included in each route. The developed preliminary design, then, stands as a basis for cost estimation and the detailed design of the structure. This would, of course, require a significant amount of time and effort. Another way to do this would be to exploit existing data on the types of bridges built in different situations using Bayesian networks. In this article, it is shown how this can be done using the information in existing databases to estimate bridge characteristics, knowing only the values of a number of situational characteristics, e.g., span to be covered, what the bridge carries, and what the bridge traverses.
  • Implications of Construction 4.0 to the workforce and organizational structures
    Item type: Journal Article
    García de Soto Lastra, Borja; Agustí-Juan, Isolda; Joss, Samuel; et al. (2022)
    International Journal of Construction Management
    The counterpart of Industry 4.0 in the AEC/FM industry is known as Construction 4.0. Its essence is the digitalization and automation of the AEC/FM industry. As robots and other technologies make their way into the different phases of the lifecycle of construction projects, the concern about the future of jobs and wages will increase. While the use of robotics has the potential to improve productivity and safety, it should not necessarily reduce total employment in the construction sector in the long run. It is expected that existing roles will evolve, and new roles will be created (e.g., in addition to designers there will be a need for employees with digital skills). Focusing on the construction phase of a robotically built concrete wall, the different roles were evaluated. From this study, it was found that there will be a time in which conventional construction and robotic technologies will coexist, leading to a higher job variability and new roles, both at the managerial and operations/execution levels. Although this study is not meant to be an exact representation of how the AEC/FM roles will change as a consequence of Construction 4.0, it opens the debate and research in this area.
  • Combining recorded failures and expert opinion in the development of ANN pipe failure prediction models
    Item type: Journal Article
    Kerwin, Sean; García de Soto Lastra, Borja; Adey, Bryan T.; et al. (2023)
    Sustainable and Resilient Infrastructure
    Buried pipes comprise a significant portion of assets of a water utility. With time, these pipes inevitably fail. Failure prediction enables infrastructure managers to estimate long-term failure trends for budgetary planning purposes and identify critical pipes for preventive intervention planning. For short-term prioritization, machine learning based algorithms appear to have superior predictive performance compared to traditional survival analysis based models. These models are typically stratified by material resulting in the exclusion of newer pipe materials such as polyethylene and corrosion-protected ductile iron, despite their prevalence in modern networks. In this paper, an application of an existing methodology is presented to estimate time to next failure using artificial neural networks (ANNs). The novelties of the approach are 1) including material as an input parameter instead of training several material-specialized models and, 2) addressing right-censored data by combining soft and hard deterioration data. The model is intended for use in short-term prioritization.
  • Using Bayesian networks to estimate bridge characteristics in early road designs
    Item type: Journal Article
    Panopoulos, Vassilis; Bougas, Apostolos; García de Soto Lastra, Borja; et al. (2021)
    Infrastructure Asset Management
    When deciding where to build new roads, it would be useful to obtain quickly and reliably an idea of the necessary characteristics of any potential bridges, using limited information and without considerable effort, as there is a considerable amount of information on built bridges in a standardised form, and there are robust algorithms for analysing these data. This study presents a methodology for estimating the likely bridge characteristics using the information available in a bridge database and Bayesian networks. The methodology is demonstrated by estimating the bridge characteristics of 1793 bridge records using nine situational characteristics – for example, the cross-section of the bridge superstructure and number of bridge spans. It is concluded that the methodology is a useful tool when estimating the characteristics of new bridges using only situational information. Compared with naïve-search databases queries, the prediction capability of all networks developed using the proposed methodology showed an estimated accuracy above 86.5%, which is considerably higher than that found when the methodology was not used – that is, 66.5%. Additionally, it is shown that Bayesian networks based on expert experience can obtain results similar to, and in many cases even better than, those of Bayesian networks based solely on learning algorithms.
  • Performance comparison for pipe failure prediction using artificial neural networks
    Item type: Conference Paper
    Kerwin, Sean; García de Soto Lastra, Borja; Adey, Bryan T. (2018)
    Life Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision
  • Determination of Optimal Rolling Planning Period for the Management of BIM-Based Construction Supply Chain Processes
    Item type: Conference Paper
    Chen, Qian; García de Soto Lastra, Borja; Adey, Bryan T. (2022)
    Construction Research Congress 2022: Project Management and Delivery, Contracts, and Design and Materials
    Current construction projects often suffer from a lack of synchronization between on-site material requirements and supply. This is at least partially because of the use of a fixed rolling planning period and the length of material lead times. The length of the rolling planning period matters because it affects when materials are ordered. The later the materials are ordered, the higher the chance of having material shortages that will cause progress delays. The earlier the materials are ordered, the higher the chance of having them delivered too early and having to organize storage and keeping track of inventory. This study proposes a methodology to determine the optimal rolling planning period for construction projects, that is, the period that provides the lowest total cost considering unexpected delays in construction progress and the unexpected need to store materials on site. The optimal period was determined using data extracted from a regularly updated building information model (BIM) and a heuristic search algorithm. The methodology is used to plan the raw materials for site-mix concrete for an office building project to be completed in four weeks. It is shown that the methodology can reduce costs related to materials arriving too early or too late on site.
  • Rethinking the roles in the AEC industry to accommodate digital fabrication
    Item type: Conference Paper
    García de Soto Lastra, Borja; Agustí-Juan, Isolda; Joss, Samuel; et al. (2018)
    Proceedings of the Creative Construction Conference (2018)
Publications 1 - 10 of 11