diff --git a/stdlib/LinearAlgebra/src/uniformscaling.jl b/stdlib/LinearAlgebra/src/uniformscaling.jl
index 57037473f4b29b..06c97ea1a34b4c 100644
--- a/stdlib/LinearAlgebra/src/uniformscaling.jl
+++ b/stdlib/LinearAlgebra/src/uniformscaling.jl
@@ -240,18 +240,18 @@ promote_to_array_type(A::Tuple{Vararg{Union{AbstractVecOrMat,UniformScaling}}})
 for (f,dim,name) in ((:hcat,1,"rows"), (:vcat,2,"cols"))
     @eval begin
         function $f(A::Union{AbstractVecOrMat,UniformScaling}...)
-            n = 0
+            n = -1
             for a in A
                 if !isa(a, UniformScaling)
                     @assert !has_offset_axes(a)
                     na = size(a,$dim)
-                    n > 0 && n != na &&
+                    n >= 0 && n != na &&
                         throw(DimensionMismatch(string("number of ", $name,
                             " of each array must match (got ", n, " and ", na, ")")))
                     n = na
                 end
             end
-            n == 0 && throw(ArgumentError($("$f of only UniformScaling objects cannot determine the matrix size")))
+            n == -1 && throw(ArgumentError($("$f of only UniformScaling objects cannot determine the matrix size")))
             return $f(promote_to_arrays(fill(n,length(A)),1, promote_to_array_type(A), A...)...)
         end
     end
@@ -262,20 +262,20 @@ function hvcat(rows::Tuple{Vararg{Int}}, A::Union{AbstractVecOrMat,UniformScalin
     @assert !has_offset_axes(A...)
     nr = length(rows)
     sum(rows) == length(A) || throw(ArgumentError("mismatch between row sizes and number of arguments"))
-    n = zeros(Int, length(A))
+    n = fill(-1, length(A))
     needcols = false # whether we also need to infer some sizes from the column count
     j = 0
     for i = 1:nr # infer UniformScaling sizes from row counts, if possible:
-        ni = 0 # number of rows in this block-row
+        ni = -1 # number of rows in this block-row, -1 indicates unknown
         for k = 1:rows[i]
             if !isa(A[j+k], UniformScaling)
                 na = size(A[j+k], 1)
-                ni > 0 && ni != na &&
+                ni >= 0 && ni != na &&
                     throw(DimensionMismatch("mismatch in number of rows"))
                 ni = na
             end
         end
-        if ni > 0
+        if ni >= 0
             for k = 1:rows[i]
                 n[j+k] = ni
             end
@@ -285,21 +285,22 @@ function hvcat(rows::Tuple{Vararg{Int}}, A::Union{AbstractVecOrMat,UniformScalin
         j += rows[i]
     end
     if needcols # some sizes still unknown, try to infer from column count
-        nc = j = 0
+        nc = -1
+        j = 0
         for i = 1:nr
             nci = 0
-            rows[i] > 0 && n[j+1] == 0 && continue # column count unknown in this row
+            rows[i] > 0 && n[j+1] == -1 && (j += rows[i]; continue)
             for k = 1:rows[i]
                 nci += isa(A[j+k], UniformScaling) ? n[j+k] : size(A[j+k], 2)
             end
-            nc > 0 && nc != nci && throw(DimensionMismatch("mismatch in number of columns"))
+            nc >= 0 && nc != nci && throw(DimensionMismatch("mismatch in number of columns"))
             nc = nci
             j += rows[i]
         end
-        nc == 0 && throw(ArgumentError("sizes of UniformScalings could not be inferred"))
+        nc == -1 && throw(ArgumentError("sizes of UniformScalings could not be inferred"))
         j = 0
         for i = 1:nr
-            if rows[i] > 0 && n[j+1] == 0 # this row consists entirely of UniformScalings
+            if rows[i] > 0 && n[j+1] == -1 # this row consists entirely of UniformScalings
                 nci = nc ÷ rows[i]
                 nci * rows[i] != nc && throw(DimensionMismatch("indivisible UniformScaling sizes"))
                 for k = 1:rows[i]
diff --git a/stdlib/LinearAlgebra/test/uniformscaling.jl b/stdlib/LinearAlgebra/test/uniformscaling.jl
index 52271177a1f96f..0852f065ae1838 100644
--- a/stdlib/LinearAlgebra/test/uniformscaling.jl
+++ b/stdlib/LinearAlgebra/test/uniformscaling.jl
@@ -183,12 +183,26 @@ end
     for T in (Matrix, SparseMatrixCSC)
         A = T(rand(3,4))
         B = T(rand(3,3))
+        C = T(rand(0,3))
+        D = T(rand(2,0))
         @test (hcat(A, 2I))::T == hcat(A, Matrix(2I, 3, 3))
         @test (vcat(A, 2I))::T == vcat(A, Matrix(2I, 4, 4))
+        @test (hcat(C, 2I))::T == C
+        @test (vcat(D, 2I))::T == D
         @test (hcat(I, 3I, A, 2I))::T == hcat(Matrix(I, 3, 3), Matrix(3I, 3, 3), A, Matrix(2I, 3, 3))
         @test (vcat(I, 3I, A, 2I))::T == vcat(Matrix(I, 4, 4), Matrix(3I, 4, 4), A, Matrix(2I, 4, 4))
         @test (hvcat((2,1,2), B, 2I, I, 3I, 4I))::T ==
             hvcat((2,1,2), B, Matrix(2I, 3, 3), Matrix(I, 6, 6), Matrix(3I, 3, 3), Matrix(4I, 3, 3))
+        @test hvcat((3,1), C, C, I, 3I)::T == hvcat((2,1), C, C, Matrix(3I, 6,6))
+        @test hvcat((2,2,2), I, 2I, 3I, 4I, C, C)::T ==
+            hvcat((2,2,2), Matrix(I, 3, 3), Matrix(2I, 3,3 ), Matrix(3I, 3,3), Matrix(4I, 3,3), C, C)
+        @test hvcat((2,2,4), C, C, I, 2I, 3I, 4I, 5I, D)::T ==
+            hvcat((2,2,4), C, C, Matrix(I, 3, 3), Matrix(2I,3,3),
+                Matrix(3I, 2, 2), Matrix(4I, 2, 2), Matrix(5I,2,2), D)
+        @test (hvcat((2,3,2), B, 2I, C, C, I, 3I, 4I))::T ==
+            hvcat((2,2,2), B, Matrix(2I, 3, 3), C, C, Matrix(3I, 3, 3), Matrix(4I, 3, 3))
+        @test hvcat((3,2,1), C, C, I, B ,3I, 2I)::T ==
+            hvcat((2,2,1), C, C, B, Matrix(3I,3,3), Matrix(2I,6,6))
     end
 end