Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algorithms may not perform as well when applied to categorical data. This article uses machine learning algorithms to predict C&D waste generation from a dataset, as a way to improve the accuracy of waste management in C&D facilities. These datasets include categorical (e.g., region, building structure, building use, wall material, and roofing material), and continuous data (particularly, gloss floor area), and a random forest (RF) algorithm was used. Results indicate that RF is an adequate machine learning algorithm for a small dataset consisting of categorical data, and even with a small dataset, an adequate prediction model can be developed. Despite the small dataset, the predictive performance according to the demolition waste (DW) type was R (Pearson’s correlation coefficient) = 0.691–0.871, R² (coefficient of determination) = 0.554–0.800, showing stable prediction performance. High prediction performance was observed using three (for mortar), five (for other DW types), or six (for concrete) input variables. This study is significant because the proposed RF model can predict DW generation using a small amount of data. Additionally, it demonstrates the possibility of applying AI to multi-purpose DW management.

This study assessed the influence of matter discharged during the production (dry/wet) of recycled aggregate on global warming potential (GWP) and acidification potential (AP), eutrophication potential (EP), ozone depletion potential (ODP), biotic resource depletion potential (ADP), photochemical ozone creation potential (POCP) using the ISO 14044 (LCA) standard. The LCIA of dry recycled aggregate was 2.94 × 10⁻² kg-CO_2eq/kg, 2.93 × 10⁻⁵ kg-SO_2eq/kg, 5.44 × 10⁻⁶ kg-PO₄³_eq/kg, 4.70 × 10⁻¹⁰ kg-CFC_11eq/kg, 1.25 × 10⁻⁵ kg-C₂H_4eq/kg, and 1.60 × 10⁻⁵ kg-Antimony_eq/kg, respectively. The environmental impact of recycled aggregate (wet) was up to 16~40% higher compared with recycled aggregate (dry); the amount of energy used by impact crushers while producing wet recycled aggregate was the main cause for this result. The environmental impact of using recycled aggregate was found to be up to twice as high as that of using natural aggregate, largely due to the greater simplicity of production of natural aggregate requiring less energy. However, ADP was approximately 20 times higher in the use of natural aggregate because doing so depletes natural resources, whereas recycled aggregate is recycled from existing construction waste. Among the life cycle impacts assessment of recycled aggregate, GWP was lower than for artificial light-weight aggregate but greater than for slag aggregate.

This study aims to compare the potential environmental impact of the manufacture and production of recycled and by-product aggregates based on a life cycle assessment and to evaluate the environmental impact and cost when they are used as aggregates in concrete. To this end, the six potential environmental impacts (i.e., abiotic depletion potential, global warming potential, ozone-layer depletion potential, acidification potential, photochemical ozone creation potential, and eutrophication potential) of the manufacture and production of natural sand, natural gravel, recycled aggregate, slag aggregate, bottom ash aggregate, and waste glass aggregate were compared using information from life cycle inventory databases. Additionally, the environmental impacts and cost were evaluated when these aggregates were used to replace 30% of the fine and coarse aggregates in concrete with a design strength of 24 MPa. The environmental impact of concrete that incorporated slag aggregate as the fine aggregates or bottom ash aggregate as the coarse aggregates were lower than that of concrete that incorporated natural aggregate. However, concrete that incorporated bottom ash aggregate as the fine aggregates demonstrated relatively high environmental impacts. Based on these environmental impacts, the environmental cost was found to range from 5.88 to 8.79 USD/m³.

To address the environmental problems associated with construction materials, the construction industry has made considerable efforts to reduce carbon emissions. However, construction materials cause several other environmental problems in addition to carbon emissions and thus, a comprehensive analysis of environmental impact categories is required. This study aims to determine the major environmental impact categories for each construction material in production stage using the life cycle assessment (LCA) technique on road projects. Through the review of life cycle impact assessment (LCIA) methodologies, the abiotic depletion potential (ADP), ozone depletion potential, photochemical oxidant creation potential, acidification potential, eutrophication potential, eco-toxicity potential, human toxicity potential, as well as the global warming potential (GWP) were defined as impact categories. To define the impact categories for road construction materials, major environmental pollutants were analyzed for a number of road projects, and impact categories for 13 major construction materials were selected as mandatory impact categories. These materials contributed more than 80% to the impact categories from an LCA perspective. The impact categories to which each material contributed more than 99% were proposed as specialization impact categories to provide basic data for use in the LCIA of future road projects.

The purpose of this study was to identify the major wastes generated during the construction phase using a life cycle assessment. To accomplish this, the amount of waste generated in the construction phase was deduced using the loss rate and weight conversions. Major construction wastes were assessed using six comprehensive environmental impact categories, including global warming potential, abiotic depletion potential, acidification potential, eutrophication potential, ozone depletion potential, and photochemical ozone creation potential. According to the analysis results, five main construction wastes—concrete, rebar, cement, polystyrene panel, and concrete block—comprehensively satisfied the 95% cutoff criteria for all six environmental impact categories. The results of the environmental impact characterization assessment revealed that concrete, concrete block, and cement waste accounted for over 70% of the contribution level in all the environmental impact categories except resource depletion. Insulation materials accounted for 1% of the total waste generated but were identified by the environmental impact assessment to have the highest contribution level.