Análisis del ciclo de vida de puentes usando matemática difusa bayesiana

Acaban de publicarnos un artículo en la revista científica Applied Sciences (indexada en el JCR, Q2) un artículo que trata sobre el análisis del ciclo de vida de puentes usando redes bayesianas y matemática difusa. El trabajo se enmarca dentro del proyecto de investigación DIMALIFE que dirijo como investigador principal en la Universitat Politècnica de València.

En la actualidad, reducir el impacto de la industria de la construcción en el medio ambiente es la clave para lograr un desarrollo sostenible. Son muchos los que utilizan software para evaluar el impacto ambiental de los puentes. Sin embargo, debido a la complejidad y discreción de los factores medioambientales de la industria de la construcción, es difícil actualizarlos y determinarlos rápidamente, y se da el fenómeno de la pérdida de datos en las bases de datos. La mayoría de los datos perdidos se optimizan mediante la simulación de Monte Carlo, lo que reduce en gran medida la fiabilidad y precisión de los resultados de la investigación. Este trabajo utiliza la teoría matemática difusa avanzada bayesiana para resolver este problema. En la investigación, se establece una evaluación de matemática difusa bayesiana y un modelo de discriminación prioritaria de sensibilidad de varios niveles, y se definen los pesos y los grados de pertenencia de los factores de influencia para lograr una cobertura completa de los factores de influencia. Con el apoyo de la modelización teórica, se evalúan exhaustivamente todos los factores de influencia de las etapas del ciclo de vida de la estructura del puente. Los resultados muestran que la fabricación de materiales, el mantenimiento y el funcionamiento del puente siguen produciendo contaminación ambiental; la fuente principal de las emisiones supera el 53% del total de las emisiones. El factor de impacto efectivo alcanza el 3,01. Al final del artículo, se estableció un modelo de sensibilidad de “big data“. Optimizando con estas técnicas, las emisiones contaminantes del tráfico se redujeron en 330 toneladas. Se confirma la eficacia y la practicidad del modelo de evaluación integral de la metodología propuesta para tratar los factores inciertos en la evaluación del desarrollo sostenible en el caso de los puentes. Los resultados de la investigación contribuye a alcanzar los objetivos de desarrollo sostenible en la industria de la construcción.

El artículo se ha publicado en abierto, y se puede descargar en el siguiente enlace: https://www.mdpi.com/2076-3417/11/11/4916

ABSTRACT:

At present, reducing the impact of the construction industry on the environment is the key to achieving sustainable development. Countries all over the world are using software systems for bridge environmental impact assessment. However, due to the complexity and discreteness of environmental factors in the construction industry, they are difficult to update and determine quickly, and there is a phenomenon of data missing in the database. Most of the lost data are optimized by Monte Carlo simulation, which greatly reduces the reliability and accuracy of the research results. This paper uses Bayesian advanced fuzzy mathematics theory to solve this problem. In the research, a Bayesian fuzzy mathematics evaluation and a multi-level sensitivity priority discrimination model are established, and the weights and membership degrees of influencing factors were defined to achieve comprehensive coverage of influencing factors. With the support of theoretical modelling, software analysis and fuzzy mathematics theory are used to comprehensively evaluate all the influencing factors of the five influencing stages in the entire life cycle of the bridge structure. The results show that the material manufacturing, maintenance, and operation of the bridge still produce environmental pollution; the main source of the emissions exceeds 53% of the total emissions. The effective impact factor reaches 3.01. At the end of the article, a big data sensitivity model was established. Through big data innovation and optimization analysis, traffic pollution emissions were reduced by 330 tonnes. Modeling of the comprehensive research model; application; clearly confirms the effectiveness and practicality of the Bayesian network fuzzy number comprehensive evaluation model in dealing with uncertain factors in the evaluation of the sustainable development of the construction industry. The research results have made important contributions to the realization of the sustainable development goals of the construction industry.

Keywords:

Construction industry; environmental; impact factor; analysis; contribution

Reference:

ZHOU, Z.; ALCALÁ, J.; KRIPKA, M.; YEPES, V. (2021). Life cycle assessment of bridges using Bayesian Networks and Fuzzy Mathematics. Applied Sciences, 11(11):4916. DOI:10.3390/app11114916

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Study on Improving Labor Productivity in the Construction Industry. The Cases of Europe and Hong Kong

Labor productivity is one the least studied areas within the construction industry. Productivity improvements achieve high cost savings with minimal investment. Due to the fact that profit margins are small on construction projects, cost savings associated with productivity are crucial to becoming a successful contractor. The chief setback to improving labor productivity is measuring labor productivity.

However, labor productivity involves many aspects. The aim of this research is to focus in some of them such as construction trades and how different factors affect their labor productivity through benchmarking in both online and hard copy format. A list of 37 construction trades was selected based on the Construction Industry Council of Hong Kong (CIC) in order to see their construction cost, labor cost and labor shortage criticality and their automation level. A list of 40 factors affecting the labor productivity was selected based on experts at The Hong Kong University of Science and Technology, in order to see in which level they affect the critical construction trades labor productivity found previously. Both results were analyzed using the relative importance index (RII).

These results are used in an additional case study, based on the comparison of them with another study with the same objectives did by some colleagues from The Hong Kong University of Science and Technology. An additional improvement of the labor productivity can be done by the mixture of both studies.

Results found previously can be used in a future study to create a tool to help contractor’s grade productivity on their projects in the preplanning stage and plan improvements in the most beneficial areas.

Reference:

ZABALLOS, I. (2016). Study on Improving Labor Productivity in the Construction Industry. The Cases of Europe and Hong Kong. Trabajo Final de Grado. Universitat Politècnica de València.

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