Thomas Mattinzioli

Bio: Thomas Mattinzioli is an academic researcher from University of Granada. The author has contributed to research in topics: Sustainable design & Ballast. The author has an hindex of 2, co-authored 5 publications receiving 12 citations.

Sustainable building rating systems: A critical review for achieving a common consensus

Abstract: Rising detrimental global environmental statistics and measurable effects have caused the sustainable building concept to gain momentum. As a result, many sustainable building rating systems and as...

Comparative Environmental Assessment of Rigid, Flexible, and Perpetual Pavements: A Case Study of Texas

Abstract: Unlike conventional pavements with a service life of 20~30 years, perpetual pavements (PPs) are designed to have a 50-year service life without requiring major maintenance and rehabilitation (M&R) activities. In this way, PPs are more cost-effective than conventional rigid pavements (CRPs) and conventional flexible pavements (CFPs). Nonetheless, even though the economic and mechanical aspects of PPs have been widely studied and well documented, the literature is limited regarding the environmental assessment of PPs. Consequently, this research estimated the environmental burden associated with five pavement structures (one CRP, one CFP, and three PP structures) through the life-cycle assessment (LCA) methodology. Notably, the PaLATE computational tool was used to carry out the LCAs. The results indicated that for CFP, most of the environmental impacts are generated by the M&R activities. Otherwise, for CRP and PP structures, the most impact occurred during the initial construction stage. The study results also revealed that materials production is the sub-stage that most contributed to the generation of environmental detriments. Overall, this comparative case study concluded that the pavement alternative with the slightest environmental damage is the PP structure.

Earth Observation for Sustainable Infrastructure: A Review

Abstract: Infrastructure is a fundamental sector for sustainable development and Earth observation has great potentials for sustainable infrastructure development (SID). However, implementations of the timely, large–scale and multi–source Earth observation are still limited in satisfying the huge global requirements of SID. This study presents a systematical literature review to identify trends of Earth observation for sustainable infrastructure (EOSI), investigate the relationship between EOSI and Sustainable Development Goals (SDGs), and explore challenges and future directions of EOSI. Results reveal the close associations of infrastructure, urban development, ecosystems, climate, Earth observation and GIS in EOSI, and indicate their relationships. In addition, from the perspective of EOSI–SDGs relationship, the huge potentials of EOSI are demonstrated from the 70% of the infrastructure influenced targets that can be directly or indirectly derived from Earth observation data, but have not been included in current SDG indicators. Finally, typical EOSI cases are presented to indicate challenges and future research directions. This review emphasizes the contributions and potentials of Earth observation to SID and EOSI is a powerful pathway to deliver on SDGs.

Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques

Abstract: Warm Mix Asphalts (WMAs) are asphalt concretes produced at lower temperatures than traditional Hot Mix Asphalts (HMAs). Nonetheless, the above is not enough to diminish the environmental impacts associated with the road infrastructure industry. Accordingly, incorporating Recycled Concrete Aggregate (RCA) as a partial replacement for Natural Aggregates (NAs) in WMA design has been gaining notoriety in the literature as a viable alternative to increase sustainability. However, the eco-friendly manufacturing of WMA with RCA contents (WMA-RCA) is not easy to obtain satisfactorily because the RCA causes alterations in the mix design. Thus, this research proposes three (3) methods to determine the optimal design conditions (coarse RCA content) that minimize the environmental burdens caused by WMA-RCA production. The first method is a mathematical model based on Multiple Linear Regression (MLR), which is used as a benchmark for the other two methods. The second and third methods are computational models based on Artificial Intelligence (AI), i.e., Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs), respectively. Notably, the Life Cycle Assessment (LCA) was employed as the theoretical framework to support all the proposed models. Consequently, this study concludes that: (i) all the proposed methodological alternatives achieve results with a great accuracy; (ii) the GAs model is the most precise method in terms of error minimization; (iii) the MLR model is the fastest method in terms of execution time; and (iv) the ANNs model is the method that requires the longest time of running, and its exactness is at a midpoint concerning the other models.