Zhenhua Wei
- Deep learning enabled particle analysis for quality assurance of construction materials. In: Automation in Construction, v. 140 (August 2022). (2022):
- Accurate prediction of concrete compressive strength based on explainable features using deep learning. In: Construction and Building Materials, v. 329 (April 2022). (2022):
- Automated detection and segmentation of concrete air voids using zero-angle light source and deep learning. In: Automation in Construction, v. 130 (Oktober 2021). (2021):
- Formalized Representation of Specifications for Construction Cost Estimation by Using Ontology. In: Computer-Aided Civil and Infrastructure Engineering, v. 31, n. 1 (Dezember 2016). (2016):
- Reduced-scale experiments to evaluate performance of composite building envelopes containing phase change materials. In: Construction and Building Materials, v. 162 (Februar 2018). (2018):
- Synthesis of nanoSiO2@graphene-oxide core-shell nanoparticles and its influence on mechanical properties of cementitious materials. In: Construction and Building Materials, v. 236 (März 2020). (2020):
- Experimental investigation on the properties and microstructure of magnesium oxychloride cement prepared with caustic magnesite and dolomite. In: Construction and Building Materials, v. 85 (Juni 2015). (2015):
- Early-age temperature evolutions in concrete pavements containing microencapsulated phase change materials. In: Construction and Building Materials, v. 147 (August 2017). (2017):
- Semi-automatic and specification-compliant cost estimation for tendering of building projects based on IFC data of design model. In: Automation in Construction, v. 30 (März 2013). (2013):