Crack　width　is　an　important　indicator　to　diagnose　the　safety　of　constructions,　e.g.,　asphalt　road,　concrete　bridge.　In　practice,　measuring　crack　width　is　a　challenge　task:　(1)　the　irregular　and　non-smooth　boundary　makes　the　traditional　method　inefficient;　(2)　pixel-wise　measurement　guarantees　the　accuracy　of　a　system　and　(3)　understanding　the　damage　of　constructions　from　any　pre-selected　points　is　a　mandatary　requirement.　To　address　these　problems,　we　propose　a　cascade　Principal　Component　Analysis　(PCA)　to　efficiently　measure　crack　width　from　images.　Firstly,　the　binary　crack　image　is　obtained　to　describe　the　crack　via　the　off-the-shelf　crack　detection　algorithms.　Secondly,　given　a　pre-selected　point,　PCA　is　used　to　find　the　main　axis　of　a　crack.　Thirdly,　Robust　Principal　Component　Analysis　(RPCA)　is　proposed　to　compute　the　main　axis　of　a　crack　with　a　irregular　boundary.　We　evaluate　the　proposed　method　on　a　real　data　set.　The　experimental　results　show　that　the　proposed　method　achieves　the　state-of-the-art　performances　in　terms　of　efficiency　and　effectiveness.　©　2018　Association　for　Computing　Machinery.